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.     

EXMOTIF: efficient structured motif extraction

Background  

Extracting motifs from sequences is a mainstay of bioinformatics. We look at the problem of mining structured motifs, which allow variable length gaps between simple motif components. We propose an efficient algorithm, called EXMOTIF, that given some sequence(s), and a structured motif template, extracts all frequent structured motifs that have quorum q. Potential applications of our method include the extraction of single/composite regulatory binding sites in DNA sequences.     

MotifCut: regulatory motifs finding with maximum density subgraphs

MOTIVATION: DNA motif finding is one of the core problems in computational biology, for which several probabilistic and discrete approaches have been developed. Most existing methods formulate motif finding as an intractable optimization problem and rely either on expectation maximization (EM) or on local heuristic searches. Another challenge is the choice of motif model: simpler models such as the position-specific scoring matrix (PSSM) impose biologically unrealistic assumptions such as independence of the motif positions, while more involved models are harder to parametrize and learn. RESULTS: We present MotifCut, a graph-theoretic approach to motif finding leading to a convex optimization problem with a polynomial time solution. We build a graph where the vertices represent all k-mers in the input sequences, and edges represent pairwise k-mer similarity. In this graph, we search for a motif as the maximum density subgraph, which is a set of k-mers that exhibit a large number of pairwise similarities. Our formulation does not make strong assumptions regarding the structure of the motif and in practice both motifs that fit well the PSSM model, and those that exhibit strong dependencies between position pairs are found as dense subgraphs. We benchmark MotifCut on both synthetic and real yeast motifs, and find that it compares favorably to existing popular methods. The ability of MotifCut to detect motifs appears to scale well with increasing input size. Moreover, the motifs we discover are different from those discovered by the other methods. AVAILABILITY: MotifCut server and other materials can be found at motifcut.stanford.edu.     

More robust detection of motifs in coexpressed genes by using phylogenetic information

Background  

Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of genes containing the motif versus the number lacking the motif might not be sufficiently high to allow detection by classical motif detection tools. To still recover motifs which are not significantly enriched but still present, we developed a procedure in which we use phylogenetic footprinting to first delineate all potential motifs in each gene. Then we mutually compare all detected motifs and identify the ones that are shared by at least a few genes in the data set as potential candidates.     

Computational generation and screening of RNA motifs in large nucleotide sequence pools

Although identification of active motifs in large random sequence pools is central to RNA in vitro selection, no systematic computational equivalent of this process has yet been developed. We develop a computational approach that combines target pool generation, motif scanning and motif screening using secondary structure analysis for applications to 10¹²–10¹⁴-sequence pools; large pool sizes are made possible using program redesign and supercomputing resources. We use the new protocol to search for aptamer and ribozyme motifs in pools up to experimental pool size (10¹⁴ sequences). We show that motif scanning, structure matching and flanking sequence analysis, respectively, reduce the initial sequence pool by 6–8, 1–2 and 1 orders of magnitude, consistent with the rare occurrence of active motifs in random pools. The final yields match the theoretical yields from probability theory for simple motifs and overestimate experimental yields, which constitute lower bounds, for aptamers because screening analyses beyond secondary structure information are not considered systematically. We also show that designed pools using our nucleotide transition probability matrices can produce higher yields for RNA ligase motifs than random pools. Our methods for generating, analyzing and designing large pools can help improve RNA design via simulation of aspects of in vitro selection.     

A computational pipeline to generate MHC binding motifs

BACKGROUND: Major histocompatibility complex (MHC) class I molecules play key roles in host immunity against pathogens by presenting peptide antigens to CD8+ T-cells. Many variants of MHC molecules exist, and each has a unique preference for certain peptide ligands. Both experimental approaches and computational algorithms have been utilized to analyze these peptide MHC binding characteristics. Traditionally, MHC binding specificities have been described in terms of binding motifs. Such motifs classify certain peptide positions as primary and secondary anchors according to their impact on binding, and they list the preferred and deleterious residues at these positions. This provides a concise and easily communicatable summary of MHC binding specificities. However, so far there has been no algorithm to generate such binding motifs in an automated and uniform fashion. In this paper, we present a computational pipeline that takes peptide MHC binding data as input and produces a concise MHC binding motif. We tested our pipeline on a set of 18 MHC class I molecules and showed that the derived motifs are consistent with historic expert assignments. We have implemented a pipeline that formally codifies rules to generate MHC binding motifs. The pipeline has been incorporated into the immune epitope database and analysis resource (IEDB) and motifs can be visualized while browsing MHC alleles in the IEDB.     

Comparison of several protein sequences by recognition of conserved regions

The sequences of related proteins show the alternance of conserved and variable regions. This fact is generally seen as a reverberation of 3 D constraints onto 1 D structures. Although the exact meaning of such constraints remains elusive, conserved regions can be extracted from protein chains and used to align them. We developed a program that efficiently performs this task. The program constructs symbolic motifs fitting a target subsequence present in every chain without requiring any insertion or deletion. However, a motif can be obliterated by substitutions when it is found in a sequence. The motifs formally consist in aminoacid symbols separated (and virtually preceded and followed) by a variable number of wild-card symbols. A wild-card, which can match any aminoacid of the chains (with no increment of score), represents a variable site within conserved regions. Different motifs are progressively built by substituting a wild-card with an aminoacid symbol within or beside preexisting motifs. Only those motifs showing an outstanding association of high matching score over all chains, and of low deviation between extreme scores over individual chains are selected for making the next generation. Starting with a null motif, the construction ends when no new aminoacid can be introduced into the current motifs. A surviving motif is then considered valid if it maps without ambiguity a unique region in every sequence, and the motif with highest score is finally selected. The construction of new motifs is then reinitated for the left and right parts of the sequences, after these have been split by the previously selected motif.(ABSTRACT TRUNCATED AT 250 WORDS)     

Motifs in brain networks

Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.