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Exploration of novel motifs derived from mouse cDNA sequences.

ABSTRACT: We performed a systematic maximum density subgraph (MDS) detection of conserved sequence regions to discover new, biologically relevant motifs from a set of 21,050 conceptually translated mouse cDNA (FANTOM1) sequences. A total of 3202 candidate sequences, which shared similar regions over >20 amino acid residues, were screened against known conserved regions listed in Pfam, ProDom, and InterPro. The filtering procedure resulted in 139 FANTOM1 sequences belonging to 49 new motif candidates. Using annotations and multiple sequence alignment information, we removed by visual inspection 42 candidates whose members were found to be false positives because of sequence redundancy, alternative splicing, low complexity, transcribed retroviral repeat elements contained in the region of the predicted open reading frame, and reports in the literature. The remaining seven motifs have been expanded by hidden Markov model (HMM) profile searches of SWISS-PROT/TrEMBL from 28 FANTOM1 sequences to 164 members and analyzed in detail on sequence and structure level to elucidate the possible functions of motifs and members. The novel and conserved motif MDS00105 is specific for the mammalian inhibitor of growth (ING) family. Three submotifs MDS00105.1-3 are specific for ING1/ING1L, ING1-homolog, and ING3 subfamilies. The motif MDS00105 together with a PHD finger domain constitutes a module for ING proteins. Structural motif MDS00113 represents a leucine zipper-like motif. Conserved motif MDS00145 is a novel 1-acyl-SN-glycerol-3-phosphate acyltransferase (AGPAT) submotif containing a transmembrane domain that distinguishes AGPAT3 and AGPAT4 from all other acyltransferase domain-containing proteins. Functional motif MDS00148 overlaps with the kazal-type serine protease inhibitor domain but has been detected only in an extracellular loop region of solute carrier 21 (SLC21) (organic anion transporters) family members, which may regulate the specificity of anion uptake. Our motif discovery not only aided in the functional characterization of new mouse orthologs for potential drug targets but also allowed us to predict that at least 16 other new motifs are waiting to be discovered from the current SWISS-PROT/TrEMBL database.

SUBMITTER: Kawaji H

PROVIDER: S-EPMC155289 | biostudies-literature | 2002 Mar

REPOSITORIES: biostudies-literature

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Exploration of novel motifs derived from mouse cDNA sequences.

Kawaji Hideya H Schönbach Christian C Matsuo Yo Y Kawai Jun J Okazaki Yasushi Y Hayashizaki Yoshihide Y Matsuda Hideo H

Genome research 20020301 3

We performed a systematic maximum density subgraph (MDS) detection of conserved sequence regions to discover new, biologically relevant motifs from a set of 21,050 conceptually translated mouse cDNA (FANTOM1) sequences. A total of 3202 candidate sequences, which shared similar regions over >20 amino acid residues, were screened against known conserved regions listed in Pfam, ProDom, and InterPro. The filtering procedure resulted in 139 FANTOM1 sequences belonging to 49 new motif candidates. Usin ...[more]

PMID: 11875024

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Project description:Envenomation of humans by snakes is a complex and continuously evolving medical emergency, and treatment is made that much more difficult by the diverse biochemical composition of many venoms. Venomous snakes and their venoms also provide models for the study of molecular evolutionary processes leading to adaptation and genotype-phenotype relationships. To compare venom complexity and protein sequences, venom gland transcriptomes are assembled, which usually requires the sacrifice of snakes for tissue. However, toxin transcripts are also present in venoms, offering the possibility of obtaining cDNA sequences directly from venom. This study provides evidence that unknown full-length venom protein transcripts can be obtained from the venoms of multiple species from all major venomous snake families. These unknown venom protein cDNAs are obtained by the use of primers designed from conserved signal peptide sequences within each venom protein superfamily. This technique was used to assemble a partial venom gland transcriptome for the Middle American Rattlesnake (Crotalus simus tzabcan) by amplifying sequences for phospholipases A2, serine proteases, C-lectins, and metalloproteinases from within venom. Phospholipase A2 sequences were also recovered from the venoms of several rattlesnakes and an elapid snake (Pseudechis porphyriacus), and three-finger toxin sequences were recovered from multiple rear-fanged snake species, demonstrating that the three major clades of advanced snakes (Elapidae, Viperidae, Colubridae) have stable mRNA present in their venoms. These cDNA sequences from venom were then used to explore potential activities derived from protein sequence similarities and evolutionary histories within these large multigene superfamilies. Venom-derived sequences can also be used to aid in characterizing venoms that lack proteomic profiles and identify sequence characteristics indicating specific envenomation profiles. This approach, requiring only venom, provides access to cDNA sequences in the absence of living specimens, even from commercial venom sources, to evaluate important regional differences in venom composition and to study snake venom protein evolution.

Project description:The protein-coding regions (coding exons) of a DNA sequence exhibit a triplet periodicity (TP) due to fact that coding exons contain a series of three nucleotide codons that encode specific amino acid residues. Such periodicity is usually not observed in introns and intergenic regions. If a DNA sequence is divided into small segments and a Fourier Transform is applied on each segment, a strong peak at frequency 1/3 is typically observed in the Fourier spectrum of coding segments, but not in non-coding regions. This property has been used in identifying the locations of protein-coding genes in unannotated sequence. The method is fast and requires no training. However, the need to compute the Fourier Transform across a segment (window) of arbitrary size affects the accuracy with which one can localize TP boundaries. Here, we report a technique that provides higher-resolution identification of these boundaries, and use the technique to explore the biological correlates of TP regions in the genome of the model organism C. elegans.Using both simulated TP signals and the real C. elegans sequence F56F11 as an example, we demonstrate that, (1) Modified Wavelet Transform (MWT) can better define the boundary of TP region than the conventional Short Time Fourier Transform (STFT); (2) The scale parameter (a) of MWT determines the precision of TP boundary localization: bigger values of a give sharper TP boundaries but result in a lower signal to noise ratio; (3) RNA splicing sites have weaker TP signals than coding region; (4) TP signals in coding region can be destroyed or recovered by frame-shift mutations; (5) 6 bp periodicities in introns and intergenic region can generate false positive signals and it can be removed with 6 bp MWT.MWT can provide more precise TP boundaries than STFT and the boundaries can be further refined by bigger scale MWT. Subtraction of 6 bp periodicity signals reduces the number of false positives. Experimentally-introduced frame-shift mutations help recover TP signal that have been lost by possible ancient frame-shifts. More importantly, TP signal has the potential to be used to detect the splice junctions in fully spliced mRNA sequence.

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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).

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Exploration of novel motifs derived from mouse cDNA sequences.

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Exploration of novel motifs derived from mouse cDNA sequences.

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