Using a library of structural templates to recognise catalytic sites and explore their evolution in homologous families

Catalytic site structure is normally highly conserved between distantly related enzymes. As a consequence, templates representing catalytic sites have the potential to succeed at function prediction in cases where methods based on sequence or overall structure fail. There are many methods for searching protein structures for matches to structural templates, but few validated template libraries to use with these methods. We present a library of structural templates representing catalytic sites, based on information from the scientific literature. Furthermore, we analyse homologous template families to discover the diversity within families and the utility of templates for active site recognition. Templates representing the catalytic sites of homologous proteins mostly differ by less than 1A root mean square deviation, even when the sequence similarity between the two proteins is low. Within these sets of homologues there is usually no discernible relationship between catalytic site structure similarity and sequence similarity. Because of this structural conservation of catalytic sites, the templates can discriminate between matches to related proteins and random matches with over 85% sensitivity and predictive accuracy. Templates based on protein backbone positions are more discriminating than those based on side-chain atoms. These analyses show encouraging prospects for prediction of functional sites in structural genomics structures of unknown function, and will be of use in analyses of convergent evolution and exploring relationships between active site geometry and chemistry. The template library can be queried via a web server at and is available for download.     

SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites

Studying similarities in protein molecules has become a fundamental activity in much of biology and biomedical research, for which methods such as multiple sequence alignments are widely used. Most methods available for such comparisons cater to studying proteins which have clearly recognizable evolutionary relationships but not to proteins that recognize the same or similar ligands but do not share similarities in their sequence or structural folds. In many cases, proteins in the latter class share structural similarities only in their binding sites. While several algorithms are available for comparing binding sites, there are none for deriving structural motifs of the binding sites, independent of the whole proteins. We report the development of SiteMotif, a new algorithm that compares binding sites from multiple proteins and derives sequence-order independent structural site motifs. We have tested the algorithm at multiple levels of complexity and demonstrate its performance in different scenarios. We have benchmarked against 3 current methods available for binding site comparison and demonstrate superior performance of our algorithm. We show that SiteMotif identifies new structural motifs of spatially conserved residues in proteins, even when there is no sequence or fold-level similarity. We expect SiteMotif to be useful for deriving key mechanistic insights into the mode of ligand interaction, predict the ligand type that a protein can bind and improve the sensitivity of functional annotation.     

Prediction of functional sites by analysis of sequence and structure conservation

We present a method for prediction of functional sites in a set of aligned protein sequences. The method selects sites which are both well conserved and clustered together in space, as inferred from the 3D structures of proteins included in the alignment. We tested the method using 86 alignments from the NCBI CDD database, where the sites of experimentally determined ligand and/or macromolecular interactions are annotated. In agreement with earlier investigations, we found that functional site predictions are most successful when overall background sequence conservation is low, such that sites under evolutionary constraint become apparent. In addition, we found that averaging of conservation values across spatially clustered sites improves predictions under certain conditions: that is, when overall conservation is relatively high and when the site in question involves a large macromolecular binding interface. Under these conditions it is better to look for clusters of conserved sites than to look for particular conserved sites.     

The sequence specificity of homeodomain-DNA interaction

The Drosophila developmental gene, engrailed, encodes a sequence-specific DNA binding activity. Using deletion constructs expressed as fusion proteins in E. coli, we localized this activity to the conserved homeodomain (HD). The binding site consensus, TCAATTAAAT, is found in clusters in the engrailed regulatory region. Weak binding of the En HD to one copy of a synthetic consensus is enhanced by adjacent copies. The distantly related HD encoded by fushi tarazu binds to the same sites as the En HD, but differs in its preference for related sites. Both HDs bind a second type of sequence, a repeat of TAA. The similarity in sequence specificity of En and Ftz HDs suggests that, within families of DNA binding proteins, close relatives will exhibit similar specificities. Competition among related regulatory proteins might govern which protein occupies a given binding site and consequently determine the ultimate effect of cis-acting regulatory sites.     

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.     

Ligand Binding Site Similarity Identification Based on Chemical and Geometric Similarity

The similarity comparison of binding sites based on amino acid between different proteins can facilitate protein function identification. However, Binding site usually consists of several crucial amino acids which are frequently dispersed among different regions of a protein and consequently make the comparison of binding sites difficult. In this study, we introduce a new method, named as chemical and geometric similarity of binding site (CGS-BSite), to compute the ligand binding site similarity based on discrete amino acids with maximum-weight bipartite matching algorithm. The principle of computing the similarity is to find a Euclidean Transformation which makes the similar amino acids approximate to each other in a geometry space, and vice versa. CGS-BSite permits site and ligand flexibilities, provides a stable prediction performance on the flexible ligand binding sites. Binding site prediction on three test datasets with CGS-BSite method has similar performance to Patch-Surfer method but outperforms other five tested methods, reaching to 0.80, 0.71 and 0.85 based on the area under the receiver operating characteristic curve, respectively. It performs a marginally better than Patch-Surfer on the binding sites with small volume and higher hydrophobicity, and presents good robustness to the variance of the volume and hydrophobicity of ligand binding sites. Overall, our method provides an alternative approach to compute the ligand binding site similarity and predict potential special ligand binding sites from the existing ligand targets based on the target similarity.     

Structure-Based Network Analysis of Activation Mechanisms in the ErbB Family of Receptor Tyrosine Kinases: The Regulatory Spine Residues Are Global Mediators of Structural Stability and Allosteric Interactions

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced “superacceptor” activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.     

Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms

Major histocompatibility complex class I (MHCI) and class II (MHCII) molecules display peptides on antigen-presenting cell surfaces for subsequent T-cell recognition. Within the human population, allelic variation among the classical MHCI and II gene products is the basis for differential peptide binding, thymic repertoire bias and allograft rejection. While available 3D structural analysis suggests that polymorphisms are found primarily within the peptide-binding site, a broader informatic approach pinpointing functional polymorphisms relevant for immune recognition is currently lacking. To this end, we have now analyzed known human class I (774) and class II (485) alleles at each amino acid position using a variability metric (V). Polymorphisms (V>1) have been identified in residues that contact the peptide and/or T-cell receptor (TCR). Using sequence logos to investigate TCR contact sites on HLA molecules, we have identified conserved MHCI residues distinct from those of conserved MHCII residues. In addition, specific class II (HLA-DP, -DQ, -DR) and class I (HLA-A, -B, -C) contacts for TCR binding are revealed. We discuss these findings in the context of TCR restriction and alloreactivity.     

Ligand binding: functional site location,similarity and docking

Computational methods for the detection and characterisation of protein ligand-binding sites have increasingly become an area of interest now that large amounts of protein structural information are becoming available prior to any knowledge of protein function. There have been particularly interesting recent developments in the following areas: first, functional site detection, whereby protein evolutionary information has been used to locate binding sites on the protein surface; second, functional site similarity, whereby structural similarity and three-dimensional templates can be used to compare and classify and potentially locate new binding sites; and third, ligand docking, which is being used to find and validate functional sites, in addition to having more conventional uses in small-molecule lead discovery.     

Roles of “junk phosphorylation” in modulating biomolecular association of phosphorylated proteins?

Protein phosphorylation dynamically regulates cellular activities in response to environmental cues. Sequence conservation analysis of recent proteome-wide phosphorylation data revealed that many previously unidentified phosphorylation sites are not well-conserved leading to the proposal that many are non-functional. However, this is based on the assumption that protein phosphorylation modulates protein function through specific position on protein sequence. Based on emerging understanding on phospho-regulation of cellular activities, we argue, with examples, that non-positionally conserved phosphorylation sites can very well be functional. We previously identified phosphorylation events that need not be conserved at same positions across orthologous proteins but are likely maintained by evolutionary conserved signaling networks through orthologous kinases. We found that proteins with such conserved phosphorylation patterns are statistically over-represented with protein and DNA-binding annotation. Here, we further correlated these proteins with protein-protein interaction data from an independent systematic study and observed they indeed interact frequently with other proteins. Hence, we speculate that non-positionally conserved phosphorylation site could be modulating biomolecular association of phosphorylated proteins possibly through fine-tuning protein’s bulk electrostatic charge and through creating binding sites for phospho-binding interaction domains. We, therefore, advocate the development of complementary evolutionary approaches to interpret physiological important sites.     

Seed selection strategy in global network alignment without destroying the entire structures of functional modules

Background

Network alignment is one of the most common biological network comparison methods. Aligning protein-protein interaction (PPI) networks of different species is of great important to detect evolutionary conserved pathways or protein complexes across species through the identification of conserved interactions, and to improve our insight into biological systems. Global network alignment (GNA) problem is NP-complete, for which only heuristic methods have been proposed so far. Generally, the current GNA methods fall into global heuristic seed-and-extend approaches. These methods can not get the best overall consistent alignment between networks for the opinionated local seed. Furthermore These methods are lost in maximizing the number of aligned edges between two networks without considering the original structures of functional modules.

Methods

We present a novel seed selection strategy for global network alignment by constructing the pairs of hub nodes of networks to be aligned into multiple seeds. Beginning from every hub seed and using the membership similarity of nodes to quantify to what extent the nodes can participate in functional modules associated with current seed topologically we align the networks by modules. By this way we can maintain the functional modules are not damaged during the heuristic alignment process. And our method is efficient in resolving the fatal problem of most conventional algorithms that the initialization selected seeds have a direct influence on the alignment result. The similarity measures between network nodes (e.g., proteins) include sequence similarity, centrality similarity, and dynamic membership similarity and our algorithm can be called Multiple Hubs-based Alignment (MHA).

Results

When applying our seed selection strategy to several pairs of real PPI networks, it is observed that our method is working to strike a balance, extending the conserved interactions while maintaining the functional modules unchanged. In the case study, we assess the effectiveness of MHA on the alignment of the yeast and fly PPI networks. Our method outperforms state-of-the-art algorithms at detecting conserved functional modules and retrieves in particular 86% more conserved interactions than IsoRank.

Conclusions

We believe that our seed selection strategy will lead us to obtain more topologically and biologically similar alignment result. And it can be used as the reference and complement of other heuristic methods to seek more meaningful alignment results.

     

Hiking in the energy landscape in sequence space: a bumpy road to good folders

With the help of a simple 20-letter lattice model of heteropolymers, we investigated the energy landscape in the space of designed good-folder sequences. Low-energy sequences form clusters, interconnected via neutral networks, in the space of sequences. Residues that play a key role in the foldability of the chain and in the stability of the native state are highly conserved, even among the chains belonging to different clusters. If, according to the interaction matrix, some strong attractive interactions are almost degenerate (i.e., they can be realized by more than one type of amino acid contacts), sequence clusters group into a few superclusters. Sequences belonging to different superclusters are dissimilar, displaying very small ( approximately 10%) similarity, and residues in key sites are, as a rule, not conserved. Similar behavior is observed in the analysis of real protein sequences.     

Prediction of the odorant binding site of olfactory receptor proteins by human-mouse comparisons

Olfactory receptors (ORs) are a large family of proteins involved in the recognition and discrimination of numerous odorants. These receptors belong to the G-protein coupled receptor (GPCR) hyperfamily, for which little structural data are available. In this study we predict the binding site residues of OR proteins by analyzing a set of 1441 OR protein sequences from mouse and human. The central insight utilized is that functional contact residues would be conserved among pairs of orthologous receptors, but considerably less conserved among paralogous pairs. Using judiciously selected subsets of 218 ortholog pairs and 518 paralog pairs, we have identified 22 sequence positions that are both highly conserved among the putative orthologs and variable among paralogs. These residues are disposed on transmembrane helices 2 to 7, and on the second extracellular loop of the receptor. Strikingly, although the prediction makes no assumption about the location of the binding site, these amino acid positions are clustered around a pocket in a structural homology model of ORs, mostly facing the inner lumen. We propose that the identified positions constitute the odorant binding site. This conclusion is supported by the observation that all but one of the predicted binding site residues correspond to ligand-contact positions in other rhodopsin-like GPCRs.     

Functional site plasticity in domain superfamilies

We present, to our knowledge, the first quantitative analysis of functional site diversity in homologous domain superfamilies. Different types of functional sites are considered separately. Our results show that most diverse superfamilies are very plastic in terms of the spatial location of their functional sites. This is especially true for protein–protein interfaces. In contrast, we confirm that catalytic sites typically occupy only a very small number of topological locations. Small-ligand binding sites are more diverse than expected, although in a more limited manner than protein–protein interfaces. In spite of the observed diversity, our results also confirm the previously reported preferential location of functional sites. We identify a subset of homologous domain superfamilies where diversity is particularly extreme, and discuss possible reasons for such plasticity, i.e. structural diversity. Our results do not contradict previous reports of preferential co-location of sites among homologues, but rather point at the importance of not ignoring other sites, especially in large and diverse superfamilies. Data on sites exploited by different relatives, within each well annotated domain superfamily, has been made accessible from the CATH website in order to highlight versatile superfamilies or superfamilies with highly preferential sites. This information is valuable for system biology and knowledge of any constraints on protein interactions could help in understanding the dynamic control of networks in which these proteins participate. The novelty of our work lies in the comprehensive nature of the analysis – we have used a significantly larger dataset than previous studies – and the fact that in many superfamilies we show that different parts of the domain surface are exploited by different relatives for ligand/protein interactions, particularly in superfamilies which are diverse in sequence and structure, an observation not previously reported on such a large scale. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.     

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence,full structure,and active site microenvironment similarity

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.     

CTCF binding site sequence differences are associated with unique regulatory and functional trends during embryonic stem cell differentiation

CTCF (CCCTC-binding factor) is a highly conserved multifunctional DNA-binding protein with thousands of binding sites genome-wide. Our previous work suggested that differences in CTCF’s binding site sequence may affect the regulation of CTCF recruitment and its function. To investigate this possibility, we characterized changes in genome-wide CTCF binding and gene expression during differentiation of mouse embryonic stem cells. After separating CTCF sites into three classes (LowOc, MedOc and HighOc) based on similarity to the consensus motif, we found that developmentally regulated CTCF binding occurs preferentially at LowOc sites, which have lower similarity to the consensus. By measuring the affinity of CTCF for selected sites, we show that sites lost during differentiation are enriched in motifs associated with weaker CTCF binding in vitro. Specifically, enrichment for T at the 18^th position of the CTCF binding site is associated with regulated binding in the LowOc class and can predictably reduce CTCF affinity for binding sites. Finally, by comparing changes in CTCF binding with changes in gene expression during differentiation, we show that LowOc and HighOc sites are associated with distinct regulatory functions. Our results suggest that the regulatory control of CTCF is dependent in part on specific motifs within its binding site.