Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Motif”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed2002

Exploration of novel motifs derived from mouse cDNA sequences.

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.

1-Acylglycerol-3-Phosphate O-Acyltransferase↗
PubMed2002

Cation-pi/H-bond stair motifs at protein-DNA interfaces.

H-bonds and cation-pi interactions between nucleic acid bases and amino acid side-chains are known to occur often concomitantly at the interface between protein and double-stranded DNA. Here we define and analyze stair-shaped motifs, which simultaneously involve base stacking, H-bond and cation-pi interactions. They consist of two successive bases along the DNA stack, one in cation-pi interaction with an amino acid side-chain that carries a total or partial positive charge, and the other H-bonded with the same side-chain. A survey of 52 high-resolution structures of protein/DNA complexes reveals the occurrence of such motifs in the majority of the complexes, the most frequent of these motifs involving Arg side-chains and G bases. These stair motifs are sometimes part of larger motifs, called multiple stair motifs, which contain several successive stairs; zinc finger proteins for example exhibit up to quadruple stairs. In another kind of stair motif extension, termed cation-pi chain motif, an amino acid side-chain or a nucleic acid base forms simultaneously two cation-pi interactions. Such a motif is observed in several homeodomains, where it involves a DNA base in cation-pi interactions with an Arg in the minor groove and an Asn in the major groove. A different cation-pi chain motif contains an Arg in cation-pi with a G and a Tyr, and is found in ets transcription factors. Still another chain motif is encountered in proteins that expulse a base from the DNA stack and replace it by an amino acid side-chain carrying a net or partial positive charge, which forms cation-pi interactions with the two neighboring bases along the DNA strand. The striking conservation of typical stair and cation-pi chain motifs within families of protein/DNA complexes suggests that they might play a structural and/or functional role and might moreover influence electron migration through the DNA double helix.

Arginine↗
PubMed2002

AUUUA motifs in the 3'UTR of human glucocorticoid receptor alpha and beta mRNA destabilize mRNA and decrease receptor protein expression.

An association between a gene polymorphism of the human glucocorticoid receptor (hGR) gene and rheumatoid arthritis has recently been suggested. This polymorphism contains an A to G mutation in the 3'UTR of exon 9beta, which encodes the 3'UTR of the mRNA of the hGRbeta isoform. The hGRbeta isoform can act as a dominant negative inhibitor of hGRalpha, and therefore may contribute to glucocorticoid resistance. The A to G mutation is located in an AUUUA motif, which is known to destabilize mRNA. In the present study, the importance of the mutation in this AUUUA motif was further characterized and mutations in other AUUUA motifs in the 3'UTR of hGRbeta and hGRalpha mRNA were studied. hGRbeta and hGRalpha expression vectors, carrying mutations in one AUUUA motif or all AUUUA motifs were transiently transfected into COS-1 cells. Each transfected vector was analyzed for the mRNA expression level, the mRNA turnover rate and the protein expression level. The naturally occurring mutation in the 3'UTR of hGRbeta mRNA increased mRNA stability and protein expression. Mutation of two other AUUUA motifs in the 3'UTR of hGRbeta, or mutation of all four AUUUA motifs resulted in a similar effect. Mutation of the most 5' AUUUA motif did not alter hGRbeta mRNA expression or mRNA stability. Mutation of all 10 AUUUA motifs in the 3'UTR of hGRalpha mRNA increased hGRalpha mRNA expression and mRNA stability as well as expression of the receptor protein level. Thus, the naturally occurring mutation in an AUUUA motif in the 3'UTR of hGRbeta mRNA results not only in increased mRNA stability, but also in increased receptor protein expression, which may contribute to glucocorticoid resistance. A similar role is suggested for two other AUUUA motifs in the 3'UTR of hGRbeta mRNA and for the 10 AUUUA motifs that are present in the 3'UTR of hGRalpha.

3' Untranslated Regions↗
PubMed1998

Leucine-based receptor sorting motifs are dependent on the spacing relative to the plasma membrane.

Many integral membrane proteins contain leucine-based motifs within their cytoplasmic domains that mediate internalization and intracellular sorting. Two types of leucine-based motifs have been identified. One type is dependent on phosphorylation, whereas the other type, which includes an acidic amino acid, is constitutively active. In this study, we have investigated how the spacing relative to the plasma membrane affects the function of both types of leucine-based motifs. For phosphorylation-dependent leucine-based motifs, a minimal spacing of 7 residues between the plasma membrane and the phospho-acceptor was required for phosphorylation and thereby activation of the motifs. For constitutively active leucine-based motifs, a minimal spacing of 6 residues between the plasma membrane and the acidic residue was required for optimal activity of the motifs. In addition, we found that the acidic residue of leucine-based motifs must be located amino-terminal to the dileucine sequence for proper function of the motifs and that residues surrounding the motifs affect the activity of the motifs. Thus, our observations suggest that the position, the exact sequence, and surrounding residues are major determinants of the function of leucine-based receptor sorting motifs.

Amino Acid Sequence↗
PubMed2002

Finding motifs using random projections.

The DNA motif discovery problem abstracts the task of discovering short, conserved sites in genomic DNA. Pevzner and Sze recently described a precise combinatorial formulation of motif discovery that motivates the following algorithmic challenge: find twenty planted occurrences of a motif of length fifteen in roughly twelve kilobases of genomic sequence, where each occurrence of the motif differs from its consensus in four randomly chosen positions. Such "subtle" motifs, though statistically highly significant, expose a weakness in existing motif-finding algorithms, which typically fail to discover them. Pevzner and Sze introduced new algorithms to solve their (15,4)-motif challenge, but these methods do not scale efficiently to more difficult problems in the same family, such as the (14,4)-, (16,5)-, and (18,6)-motif problems. We introduce a novel motif-discovery algorithm, PROJECTION, designed to enhance the performance of existing motif finders using random projections of the input's substrings. Experiments on synthetic data demonstrate that PROJECTION remedies the weakness observed in existing algorithms, typically solving the difficult (14,4)-, (16,5)-, and (18,6)-motif problems. Our algorithm is robust to nonuniform background sequence distributions and scales to larger amounts of sequence than that specified in the original challenge. A probabilistic estimate suggests that related motif-finding problems that PROJECTION fails to solve are in all likelihood inherently intractable. We also test the performance of our algorithm on realistic biological examples, including transcription factor binding sites in eukaryotes and ribosome binding sites in prokaryotes.

Algorithms↗
PubMed2004

Topological generalizations of network motifs.

Biological and technological networks contain patterns, termed network motifs, which occur far more often than in randomized networks. Network motifs were suggested to be elementary building blocks that carry out key functions in the network. It is of interest to understand how network motifs combine to form larger structures. To address this, we present a systematic approach to define "motif generalizations": families of motifs of different sizes that share a common architectural theme. To define motif generalizations, we first define "roles" in a subgraph according to structural equivalence. For example, the feedforward loop triad--a motif in transcription, neuronal, and some electronic networks--has three roles: an input node, an output node, and an internal node. The roles are used to define possible generalizations of the motif. The feedforward loop can have three simple generalizations, based on replicating each of the three roles and their connections. We present algorithms for efficiently detecting motif generalizations. We find that the transcription networks of bacteria and yeast display only one of the three generalizations, the multi-output feedforward generalization. In contrast, the neuronal network of C. elegans mainly displays the multi-input generalization. Forward-logic electronic circuits display a multi-input, multi-output hybrid. Thus, networks which share a common motif can have very different generalizations of that motif. Using mathematical modeling, we describe the information processing functions of the different motif generalizations in transcription, neuronal, and electronic networks.

Algorithms↗
PubMed1990

Octamer and SPH motifs in the U1 enhancer cooperate to activate U1 RNA gene expression.

The transcriptional enhancer of a chicken U1 small nuclear RNA gene has been shown to extend over approximately 50 base pairs of DNA sequence located 180 to 230 base pairs upstream of the U1 transcription initiation site. It is composed of multiple functional motifs, including a GC box, an octamer motif, and a novel SPH motif. The contributions of these three distinct sequence motifs to enhancer function were studied with an oocyte expression assay. Under noncompetitive conditions in oocytes, the SPH motif is capable of stimulating U1 RNA transcription in the absence of the other functional motifs, whereas the octamer motif by itself lacks this ability. However, to form a transcription complex that is stable to challenge by a second competing small nuclear RNA transcription unit, both the octamer and SPH motifs are required. The GC box, although required for full enhancer activity, is not essential for stable complex formation in oocytes. Site-directed mutagenesis was used to study the DNA sequence requirements of the SPH motif. Functional activity of the SPH motif is spread throughout a 24-base-pair region 3' of the octamer but is particularly dependent upon sequences near an SphI restriction site located at the center of the SPH motif. Using embryonic chicken tissue as a source material, we identified and partially purified a factor, termed SBF, that binds sequence specifically to the SPH motif of the U1 enhancer. The ability of this factor to recognize and bind to mutant enhancer DNA fragments in vitro correlates with the functional activity of the corresponding enhancer sequences in vivo.

Animals↗
PubMed2006

Cis-motifs upstream of the transcription and translation initiation sites are effectively revealed by their positional disequilibrium in eukaryote genomes using frequency distribution curves.

BACKGROUND: The discovery of cis-regulatory motifs still remains a challenging task even though the number of sequenced genomes is constantly growing. Computational analyses using pattern search algorithms have been valuable in phylogenetic footprinting approaches as have expression profile experiments to predict co-occurring motifs. Surprisingly little is known about the nature of cis-regulatory element (CRE) distribution in promoters. RESULTS: In this paper we used the Motif Mapper open-source collection of visual basic scripts for the analysis of motifs in any aligned set of DNA sequences. We focused on promoter motif distribution curves to identify positional over-representation of DNA motifs. Using differentially aligned datasets from the model species Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster and Saccharomyces cerevisiae, we convincingly demonstrated the importance of the position and orientation for motif discovery. Analysis with known CREs and all possible hexanucleotides showed that some functional elements gather close to the transcription and translation initiation sites and that elements other than the TATA-box motif are conserved between eukaryote promoters. While a high background frequency usually decreases the effectiveness of such an enumerative investigation, we improved our analysis by conducting motif distribution maps using large datasets. CONCLUSION: This is the first study to reveal positional over-representation of CREs and promoter motifs in a cross-species approach. CREs and motifs shared between eukaryotic promoters support the observation that an eukaryotic promoter structure has been conserved throughout evolutionary time. Furthermore, with the information on positional enrichment of a motif or a known functional CRE, it is possible to get a more detailed insight into where an element appears to function. This in turn might accelerate the in depth examination of known and yet unknown cis-regulatory sequences in the laboratory.

Algorithms↗
PubMed2005

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.

Amino Acid Motifs↗
PubMed2006

Novel FXXFF and FXXMF motifs in androgen receptor cofactors mediate high affinity and specific interactions with the ligand-binding domain.

Upon hormone binding, a hydrophobic coactivator binding groove is induced in the androgen receptor (AR) ligand-binding domain (LBD). This groove serves as high affinity docking site for alpha-helical FXXLF motifs present in the AR N-terminal domain and in AR cofactors. Study of the amino acid requirements at position +4 of the AR FXXLF motif revealed that most amino acid substitutions strongly reduced or completely abrogated AR LBD interaction. Strong interactions were still observed following substitution of Leu+4 by Phe or Met residues. Leu+4 to Met or Phe substitutions in the FXXLF motifs of AR cofactors ARA54 and ARA70 were also compatible with strong AR LBD binding. Like the corresponding FXXLF motifs, interactions of FXXFF and FXXMF variants of AR and ARA54 motifs were AR specific, whereas variants of the less AR-selective ARA70 motif displayed increased AR specificity. A survey of currently known AR-binding proteins revealed the presence of an FXXFF motif in gelsolin and an FXXMF motif in PAK6. In vivo fluorescence resonance energy transfer and functional protein-protein interaction assays showed direct, efficient, and specific interactions of both motifs with AR LBD. Mutation of these motifs abrogated interaction of gelsolin and PAK6 proteins with AR. In conclusion, we have demonstrated strong interaction of FXXFF and FXXMF motifs to the AR coactivator binding groove, thereby mediating specific binding of a subgroup of cofactors to the AR LBD.

Amino Acid Motifs↗
PubMedDate not supplied

Finding motifs with insufficient number of strong binding sites.

A molecule called transcription factor usually binds to a set of promoter sequences of coexpressed genes. As a result, these promoter sequences contain some short substrings, or binding sites, with similar patterns. The motif discovering problem is to find these similar patterns and motifs in a set of sequences. Most existing algorithms find the motifs based on strong-signal sequences only (i.e., those containing binding sites very similar to the motif). In this paper, we use a probability matrix to represent a motif to calculate the minimum total number of binding sites required to be in the input dataset in order to confirm that the discovered motifs are not artifacts. Next, we introduce a more general and realistic energy-based model, which considers all sequences with varying degrees of binding strength to the transcription factors (as measured experimentally). By treating sequences with varying degrees of binding strength, we develop a heuristic algorithm called EBMF (Energy-Based Motif Finding Algorithm) to find the motif, which can handle sequences ranging from those that contain more than one binding site to those that contain none. EBMF can find motifs for datasets that do not even have the required minimum number of binding sites as previously derived. EBMF compares favorably with common motif-finding programs AlignACE and MEME. In particular, for some simulated and real datasets, EBMF finds the motif when both AlignACE and MEME fail to do so.

Algorithms↗
PubMed2025

MAFin: motif detection in multiple alignment files.

MOTIVATION: Whole Genome and Proteome Alignments, represented by the multiple alignment file format, have become a standard approach in comparative genomics and proteomics. These often require identifying conserved motifs, which is crucial for understanding functional and evolutionary relationships. However, current approaches lack a direct method for motif detection within MAF files. We present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files to address this gap, streamlining genomic and proteomic research. RESULTS: We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: (i) using user-specified k-mers to search the sequences. (ii) with regular expressions, in which case one or more patterns are searched, and (iii) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enables the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses. AVAILABILITY AND IMPLEMENTATION: MAFin is offered as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.

Software↗
PubMed2005

Improved detection of DNA motifs using a self-organized clustering of familial binding profiles.

MOTIVATION: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be 'biased' towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs. RESULTS: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run. AVAILABILITY: The methods outlined will be incorporated into the next release of SOMBRERO, which is available from http://bioinf.nuigalway.ie/sombrero

Algorithms↗
PubMed2006

MUSA: a parameter free algorithm for the identification of biologically significant motifs.

MOTIVATION: The ability to identify complex motifs, i.e. non-contiguous nucleotide sequences, is a key feature of modern motif finders. Addressing this problem is extremely important, not only because these motifs can accurately model biological phenomena but because its extraction is highly dependent upon the appropriate selection of numerous search parameters. Currently available combinatorial algorithms have proved to be highly efficient in exhaustively enumerating motifs (including complex motifs), which fulfill certain extraction criteria. However, one major problem with these methods is the large number of parameters that need to be specified. RESULTS: We propose a new algorithm, MUSA (Motif finding using an UnSupervised Approach), that can be used either to autonomously find over-represented complex motifs or to estimate search parameters for modern motif finders. This method relies on a biclustering algorithm that operates on a matrix of co-occurrences of small motifs. The performance of this method is independent of the composite structure of the motifs being sought, making few assumptions about their characteristics. The MUSA algorithm was applied to two datasets involving the bacterium Pseudomonas putida KT2440. The first one was composed of 70 sigma(54)-dependent promoter sequences and the second dataset included 54 promoter sequences of up-regulated genes in response to phenol, as suggested by quantitative proteomics. The results obtained indicate that this approach is very effective at identifying complex motifs of biological significance. AVAILABILITY: The MUSA algorithm is available upon request from the authors, and will be made available via a Web based interface.

Algorithms↗
PubMed2005

Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property.

Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved K-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved K-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved K-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional K-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved K-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new K-means algorithm may be applied to other areas of bioinformatics research in order to explore the underlying relationships between data samples more effectively.

Algorithms↗
PubMed2004

Automatic annotation of protein motif function with Gene Ontology terms.

BACKGROUND: Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. RESULTS: This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. CONCLUSIONS: In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

Amino Acid Motifs↗
PubMed2005

The EH1 motif in metazoan transcription factors.

BACKGROUND: The Engrailed Homology 1 (EH1) motif is a small region, believed to have evolved convergently in homeobox and forkhead containing proteins, that interacts with the Drosophila protein groucho (C. elegans unc-37, Human Transducin-like Enhancers of Split). The small size of the motif makes its reliable identification by computational means difficult. I have systematically searched the predicted proteomes of Drosophila, C. elegans and human for further instances of the motif. RESULTS: Using motif identification methods and database searching techniques, I delimit which homeobox and forkhead domain containing proteins also have likely EH1 motifs. I show that despite low database search scores, there is a significant association of the motif with transcription factor function. I further show that likely EH1 motifs are found in combination with T-Box, Zinc Finger and Doublesex domains as well as discussing other plausible candidate associations. I identify strong candidate EH1 motifs in basal metazoan phyla. CONCLUSION: Candidate EH1 motifs exist in combination with a variety of transcription factor domains, suggesting that these proteins have repressor functions. The distribution of the EH1 motif is suggestive of convergent evolution, although in many cases, the motif has been conserved throughout bilaterian orthologs. Groucho mediated repression was established prior to the evolution of bilateria.

Amino Acid Motifs↗
PubMed2004

Analysis of the role of the leucine zipper motif in regulating the ability of AFAP-110 to alter actin filament integrity.

AFAP-110 has an intrinsic ability to alter actin filament integrity as an actin filament crosslinking protein. This capability is regulated by a carboxy terminal leucine zipper (Lzip) motif. The Lzip motif facilitates self-association stabilizing the AFAP-110 multimers. Deletion of the Lzip motif (AFAP-110(Deltalzip)) reduces the stability of the AFAP-110 multimer and concomitantly increases its ability to crosslink actin filaments, in vitro, and to activate cSrc and alter actin filament integrity, in vivo. We sought to determine how the Lzip motif regulates AFAP-110 function. Substitution of the c-Fos Lzip motif in place of the AFAP-110 Lzip motif (AFAP-110(fos)) was predicted to preserve the alpha-helical structure while changing the sequence. To alter the structure of the alpha-helix, a leucine to proline mutation was generated in the AFAP-110 alpha-helical Lzip motif (AFAP-110(581P)), which largely preserved the sequence. The helix mutants, AFAP-110(Deltalzip), AFAP-110(fos), and AFAP-110(581P), demonstrated reduced multimer stability with an increased capacity to crosslink actin filaments, in vitro, relative to AFAP-110. An analysis of opposing binding sites indicated that the carboxy terminus/Lzip motif can contact sequences within the amino terminal pleckstrin homology (PH1) domain indicating an auto-inhibitory mechanism for regulating multimer stability and actin filament crosslinking. In vivo, only AFAP-110(Deltalzip) and AFAP-110(581P) were to activate cSrc and to alter cellular actin filament integrity. These data indicate that the intrinsic ability of AFAP-110 to crosslink actin filaments is dependent upon both the sequence and structure of the Lzip motif, while the ability of the Lzip motif to regulate AFAP-110-directed activation of cSrc and changes in actin filament integrity in vivo is dependent upon the structure or presence of the Lzip motif. We hypothesize that the intrinsic ability of AFAP-110 to crosslink actin filaments or activate cSrc are distinct functions.

Actin Cytoskeleton↗