Equivalent binding sites reveal convergently evolved interaction motifs

MOTIVATION: Much research has been devoted to the characterization of interaction interfaces found in complexes with known structure. In this context, the interactions of non-homologous domains at equivalent binding sites are of particular interest, as they can reveal convergently evolved interface motifs. Such motifs are an important source of information to formulate rules for interaction specificity and to design ligands based on the common features shared among diverse partners. RESULTS: We develop a novel method to identify non-homologous structural domains which bind at equivalent sites when interacting with a common partner. We systematically apply this method to all pairs of interactions with known structure and derive a comprehensive database for these interactions. Of all non-homologous domains, which bind with a common interaction partner, 4.2% use the same interface of the common interaction partner (excluding immunoglobulins and proteases). This rises to 16% if immunoglobulin and proteases are included. We demonstrate two applications of our database: first, the systematic screening for viral protein interfaces, which can mimic native interfaces and thus interfere; and second, structural motifs in enzymes and its inhibitors. We highlight several cases of virus protein mimicry: viral M3 protein interferes with a chemokine dimer interface. The virus has evolved the motif SVSPLP, which mimics the native SSDTTP motif. A second example is the regulatory factor Nef in HIV which can mimic a kinase when interacting with SH3. Among others the virus has evolved the kinase's PxxP motif. Further, we elucidate motif resemblances in Baculovirus p35 and HIV capsid proteins. Finally, chymotrypsin is subject to scrutiny wrt. its structural similarity to subtilisin and wrt. its inhibitor's similar recognition sites. SUPPLEMENTARY INFORMATION: A database is online at scoppi.biotec.tu-dresden.de/abac/.     

Predictable cholesterol binding sites in GPCRs lack consensus motifs

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Finding subtle motifs by branching from sample strings

Many motif finding algorithms apply local search techniques to a set of seeds. For example, GibbsDNA (Lawrence et al. 1993, Science, 262, 208-214) applies Gibbs sampling to random seeds, and MEME (Bailey and Elkan, 1994, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB-94), 28-36) applies the EM algorithm to selected sample strings, i.e. substrings of the sample. In the case of subtle motifs, recent benchmarking efforts show that both random seeds and selected sample strings may never get close to the globally optimal motif. We propose a new approach which searches motif space by branching from sample strings, and implement this idea in both pattern-based and profile-based settings. Our PatternBranching and ProfileBranching algorithms achieve favorable results relative to other motif finding algorithms. Availability: http://www-cse.ucsd.edu/groups/bioinformatics/software.html     

Computer simulation of zinc finger motifs from cellular nucleic acid binding protein and their interaction with consensus DNA sequences

We report here a computer simulation of the three-dimensional structures of seven zinc finger motifs from cellular nucleic acid binding protein involved in negative feedback inhibition of cholesterol biosynthesis. The structures are optimised using steric constraints imposed by tetrahedral coordination of the zinc ion with Cys and His residues, by molecular mechanics technique. We have also optimised the structure of a finger-I with GpT sequence. The model for the interaction of seven fingered protein with single-stranded d(GTGCGGTG) from sterol regulatory element (SRE) is given on the basis of these results. We also propose a scheme for recognition of a multifingered regulatory protein with small single-stranded DNA fragments.     

Probabilistic alignment of motifs with sequences

MOTIVATION: Motif detection is an important component of the classification and annotation of protein sequences. A method for aligning motifs with an amino acid sequence is introduced. The motifs can be described by the secondary (i.e. functional, biophysical, etc.) characteristics of a signal or pattern to be detected. The results produced are based on the statistical relevance of the alignment. The method was targeted to avoid the problems (i.e. over-fitting, biological interpretation and mathematical soundness) encountered in other methods currently available. RESULTS: The method was tested on lipoprotein signals in B. subtilis yielding stable results. The results of signal prediction were consistent with other methods where literature was available. AVAILABILITY: An implementation of the motif alignment, refining and bootstrapping is available for public use online at http://www.expasy.org/tools/patoseq/     

Tree Gibbs Sampler: identifying conserved motifs without aligning orthologous sequences

SUMMARY: Tree Gibbs Sampler is a software for identifying motifs by simultaneously using the motif overrepresentation property and the motif evolutionary conservation property. It identifies motifs without depending on pre-aligned orthologous sequences, which makes it useful for the extraction of regulatory elements in multiple genomes of both closely related and distant species. AVAILABILITY: The Tree Gibbs Sampler software is freely downloadable at https://compbio.iupui.edu/xiaomanli/LiSoftware/retrieve.php?ID=tgs     

Information content of binding sites on nucleotide sequences

Repressors, polymerases, ribosomes and other macromolecules bind to specific nucleic acid sequences. They can find a binding site only if the sequence has a recognizable pattern. We define a measure of the information (R sequence) in the sequence patterns at binding sites. It allows one to investigate how information is distributed across the sites and to compare one site to another. One can also calculate the amount of information (R frequency) that would be required to locate the sites, given that they occur with some frequency in the genome. Several Escherichia coli binding sites were analyzed using these two independent empirical measurements. The two amounts of information are similar for most of the sites we analyzed. In contrast, bacteriophage T7 RNA polymerase binding sites contain about twice as much information as is necessary for recognition by the T7 polymerase, suggesting that a second protein may bind at T7 promoters. The extra information can be accounted for by a strong symmetry element found at the T7 promoters. This element may be an operator. If this model is correct, these promoters and operators do not share much information. The comparisons between R sequence and R frequency suggest that the information at binding sites is just sufficient for the sites to be distinguished from the rest of the genome.     

RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites

RNA molecules can adopt stable secondary and tertiary structures, which are essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via coarse-grained molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28B. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.     

Finding the most significant common sequence and structure motifs in a set of RNA sequences.

We present a computational scheme to locally align a collection of RNA sequences using sequence and structure constraints. In addition, the method searches for the resulting alignments with the most significant common motifs, among all possible collections. The first part utilizes a simplified version of the Sankoff algorithm for simultaneous folding and alignment of RNA sequences, but maintains tractability by constructing multi-sequence alignments from pairwise comparisons. The algorithm finds the multiple alignments using a greedy approach and has similarities to both CLUSTAL and CONSENSUS, but the core algorithm assures that the pairwise alignments are optimized for both sequence and structure conservation. The choice of scoring system and the method of progressively constructing the final solution are important considerations that are discussed. Example solutions, and comparisons with other approaches, are provided. The solutions include finding consensus structures identical to published ones.     

WildSpan: mining structured motifs from protein sequences

Background  

Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.     

Daunomycin binding to deoxypolynucleotides with alternating sequences: complete thermodynamic profiles of heterogeneous binding sites

Complete thermodynamic binding profiles for the interaction of the anticancer drug, daunomycin with natural DNA and synthetic deoxypolynucleotides were described. Fluorescence titration method was used to estimate the equilibrium binding constants. Binding isotherms were found to be surprisingly complex in some cases, presumably because there were heterogeneous sites even in simple deoxypolynucleotides of repeating sequence. Some polynucleotides consisting of alternating sequence contain at least two different binding sites for daunomycin. The binding affinity of the primary binding sites of alternating and non-alternating sequences was found to differ by two orders of magnitude. An isothermal microtitration calorimeter was used to directly measure the binding enthalpy at 25 degrees C with a high sensitivity. The binding enthalpy of poly[d(A-T)] was found to be -5.5 Kcal/mol, which was much lower than any other polynucleotides, while the binding constant of the high affinity sites, was similar. In this report, the complete thermodynamic profiles of daunomycin binding to deoxypolynucleotides were reliably shown for the first time.     

Predicting protein functional sites with phylogenetic motifs

In this report, we demonstrate that phylogenetic motifs, sequence regions conserving the overall familial phylogeny, represent a promising approach to protein functional site prediction. Across our structurally and functionally heterogeneous data set, phylogenetic motifs consistently correspond to functional sites defined by both surface loops and active site clefts. Additionally, the partially buried prosthetic group regions of cytochrome P450 and succinate dehydrogenase are identified as phylogenetic motifs. In nearly all instances, phylogenetic motifs are structurally clustered, despite little overall sequence proximity, around key functional site features. Based on calculated false-positive expectations and standard motif identification methods, we show that phylogenetic motifs are generally conserved in sequence. This result implies that they can be considered motifs in the traditional sense as well. However, there are instances where phylogenetic motifs are not (overall) well conserved in sequence. This point is enticing, because it implies that phylogenetic motifs are able to identify key sequence regions that traditional motif-based approaches would not. Further, phylogenetic motif results are also shown to be consistent with evolutionary trace results, and bootstrapping is used to demonstrate tree significance.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

Automatic generation of 3D motifs for classification of protein binding sites

Background  

Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands.     

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.