Mining tertiary structural motifs for assessment of designability.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang J
PROVIDER: S-EPMC4222026 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Mining tertiary structural motifs for assessment of designability.
Zhang Jian J Grigoryan Gevorg G
Methods in enzymology 20130101
The observation of a limited secondary-structural alphabet in native proteins, with significant sequence preferences, has profoundly influenced the fields of protein design and structure prediction (Simons, Kooperberg, Huang, & Baker, 1997; Verschueren et al., 2011). In the era of structural genomics, as the size of the structural dataset continues to grow rapidly, it is becoming possible to extend this analysis to tertiary structural motifs and their sequences. For a hypothetical tertiary motif ...[more]
Similar Datasets
Project description:The Protein Data Bank (PDB) has been a key resource for learning general rules of sequence-structure relationships in proteins. Quantitative insights have been gained by defining geometric descriptors of structure (e.g., distances, dihedral angles, solvent exposure, etc.) and observing their distributions and sequence preferences. Here we argue that as the PDB continues to grow, it may become unnecessary to reduce structure into a set of elementary descriptors. Instead, it could be possible to deduce quantitative sequence-structure relationships in the context of precisely-defined complex structural motifs by mining the PDB for closely matching backbone geometries. To validate this idea, we turned to the the task of predicting changes in protein stability upon amino-acid substitution-a difficult problem of broad significance. We defined non-contiguous tertiary motifs (TERMs) around a protein site of interest and extracted sequence preferences from ensembles of closely-matching substructures in the PDB to predict mutational stability changes at the site, ??Gm. We demonstrate that these ensemble statistics predict ??Gm on par with state-of-the-art statistical and machine-learning methods on large thermodynamic datasets, and outperform these, along with a leading structure-based modeling approach, when tested in the context of unbiased diverse mutations. Further, we show that the performance of the TERM-based method is directly related to the amount of available relevant structural data, automatically improving with the growing PDB. This enables a means of estimating prediction accuracy. Our results clearly demonstrate that: 1) statistics of non-contiguous structural motifs in the PDB encode fundamental sequence-structure relationships related to protein thermodynamic stability, and 2) the PDB is now large enough that such statistics are already useful in practice, with their accuracy expected to continue increasing as the database grows. These observations suggest new ways of using structural data towards addressing problems of computational structural biology.
| S-EPMC5446159 | biostudies-literature
Project description:BackgroundAutomatic 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.ResultsWildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm.ConclusionsThe mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan).
| S-EPMC3082213 | biostudies-literature
Project description:BackgroundMotif patterns of maximal saturation emerged originally in contexts of pattern discovery in biomolecular sequences and have recently proven a valuable notion also in the design of data compression schemes. Informally, a motif is a string of intermittently solid and wild characters that recurs more or less frequently in an input sequence or family of sequences. Motif discovery techniques and tools tend to be computationally imposing, however, special classes of "rigid" motifs have been identified of which the discovery is affordable in low polynomial time.ResultsIn the present work, "extensible" motifs are considered such that each sequence of gaps comes endowed with some elasticity, whereby the same pattern may be stretched to fit segments of the source that match all the solid characters but are otherwise of different lengths. A few applications of this notion are then described. In applications of data compression by textual substitution, extensible motifs are seen to bring savings on the size of the codebook, and hence to improve compression. In germane contexts, in which compressibility is used in its dual role as a basis for structural inference and classification, extensible motifs are seen to support unsupervised classification and phylogeny reconstruction.ConclusionOff-line compression based on extensible motifs can be used advantageously to compress and classify biological sequences.
| S-EPMC1459173 | biostudies-literature
Project description:A new approach, graph-grammars, to encode RNA tertiary structure patterns is introduced and exemplified with the classical sarcin-ricin motif. The sarcin-ricin motif is found in the stem of the crucial ribosomal loop E (also referred to as the sarcin-ricin loop), which is sensitive to the alpha-sarcin and ricin toxins. Here, we generate a graph-grammar for the sarcin-ricin motif and apply it to derive putative sequences that would fold in this motif. The biological relevance of the derived sequences is confirmed by a comparison with those found in known sarcin-ricin sites in an alignment of over 800 bacterial 23S ribosomal RNAs. The comparison raised alternative alignments in few sarcin-ricin sites, which were assessed using tertiary structure predictions and 3D modeling. The sarcin-ricin motif graph-grammar was built with indivisible nucleotide interaction cycles that were recently observed in structured RNAs. A comparison of the sequences and 3D structures of each cycle that constitute the sarcin-ricin motif gave us additional insights about RNA sequence-structure relationships. In particular, this analysis revealed the sequence space of an RNA motif depends on a structural context that goes beyond the single base pairing and base-stacking interactions.
| S-EPMC1865062 | biostudies-literature
Project description:As a major driving force of genome evolution, transposons have been deviating from their original connotation as "junk" DNA ever since their important roles were revealed. The recently discovered Helitron transposons have been investigated in diverse eukaryotic genomes because of their remarkable gene-capture ability and other features that are crucial to our current understanding of genome dynamics. Helitrons are not canonical transposons in that they do not end in inverted repeats or create target site duplications, which makes them difficult to identify. Previous methods mainly rely on sequence alignment of conserved Helitron termini or manual curation. The abundance of Helitrons in genomes is still underestimated. We developed an automated and generalized tool, HelitronScanner, that identified a plethora of divergent Helitrons in many plant genomes. A local combinational variable approach as the key component of HelitronScanner offers a more granular representation of conserved nucleotide combinations and therefore is more sensitive in finding divergent Helitrons. This commentary provides an in-depth view of the local combinational variable approach and its association with Helitron sequence patterns. Analysis of Helitron terminal sequences shows that the local combinational variable approach is an efficacious representation of nucleotide patterns imperceptible at a full-sequence level.
| S-EPMC4588551 | biostudies-other
Project description:BackgroundIn protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class.ResultsWe present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks.ConclusionThe proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.
| S-EPMC3610217 | biostudies-literature
Project description:BACKGROUND:Glycans are complex sugar chains, crucial to many biological processes. By participating in binding interactions with proteins, glycans often play key roles in host-pathogen interactions. The specificities of glycan-binding proteins, such as lectins and antibodies, are governed by motifs within larger glycan structures, and improved characterisations of these determinants would aid research into human diseases. Identification of motifs has previously been approached as a frequent subtree mining problem, and we extend these approaches with a glycan notation that allows recognition of terminal motifs. RESULTS:In this work, we customised a frequent subtree mining approach by altering the glycan notation to include information on terminal connections. This allows specific identification of terminal residues as potential motifs, better capturing the complexity of glycan-binding interactions. We achieved this by including additional nodes in a graph representation of the glycan structure to indicate the presence or absence of a linkage at particular backbone carbon positions. Combining this frequent subtree mining approach with a state-of-the-art feature selection algorithm termed minimum-redundancy, maximum-relevance (mRMR), we have generated a classification pipeline that is trained on data from a glycan microarray. When applied to a set of commonly used lectins, the identified motifs were consistent with known binding determinants. Furthermore, logistic regression classifiers trained using these motifs performed well across most lectins examined, with a median AUC value of 0.89. CONCLUSIONS:We present here a new subtree mining approach for the classification of glycan binding and identification of potential binding motifs. The Carbohydrate Classification Accounting for Restricted Linkages (CCARL) method will assist in the interpretation of glycan microarray experiments and will aid in the discovery of novel binding motifs for further experimental characterisation.
| S-EPMC7001330 | biostudies-literature
Project description:Fold designability has been estimated by the number of families contained in that fold. Here, we show that among orthologous proteins, sequence divergence is higher for folds with greater numbers of families. Folds with greater numbers of families also tend to have families that appear more often in the proteome and greater promiscuity (the number of unique "partner" folds that the fold is found with within the same protein). We also find that many disease-related proteins have folds with relatively few families. In particular, a number of these proteins are associated with diseases occurring at high frequency. These results suggest that family counts reflect how certain structures are distributed in nature and is an important characteristic associated with many human diseases.
| S-EPMC1456317 | biostudies-literature
Project description:A typical protein structure is a compact packing of connected alpha-helices and/or beta-strands. We have developed a method for generating the ensemble of compact structures a given set of helices and strands can form. The method is tested on structures composed of four alpha-helices connected by short turns. All such natural four-helix bundles that are connected by short turns seen in nature are reproduced to closer than 3.6 A per residue within the ensemble. Because structures with no natural counterpart may be targets for ab initio structure design, the designability of each structure in the ensemble-defined as the number of sequences with that structure as their lowest-energy state-is evaluated using a hydrophobic energy. For the case of four alpha-helices, a small set of highly designable structures emerges, most of which have an analog among the known four-helix fold families; however, several packings and topologies with no analogs in protein database are identified.
| S-EPMC123227 | biostudies-literature
Project description:The modulation of protein-protein interactions (PPIs) by means of creating or stabilizing secondary structure conformations is a rapidly growing area of research. Recent success in the inhibition of difficult PPIs by secondary structure mimetics also points to potential limitations, because often, specific cases require tertiary structure mimetics. To streamline protein structure-based inhibitor design, we have previously described the examination of protein complexes in the Protein Data Bank where α-helices or β-strands form critical contacts. Here, we examined coiled coils and helix bundles that mediate complex formation to create a platform for the discovery of potential tertiary structure mimetics. Though there has been extensive analysis of coiled coil motifs, the interactions between pre-formed coiled coils and globular proteins have not been systematically analyzed. This article identifies critical features of these helical interfaces with respect to coiled coil and other helical PPIs. We expect the analysis to prove useful for the rational design of modulators of this fundamental class of protein assemblies.
| S-EPMC4577960 | biostudies-literature