Acceleration of α‐synuclein aggregation by homologous peptides
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α‐Synuclein (α‐Syn), amyloid β‐protein and prion protein are among the amyloidogenic proteins that are associated with the neurodegenerative diseases. These three proteins share a homologous region with a consensus sequence mainly consisting of glycine, alanine and valine residues (accordingly named as the GAV motif), which was proposed to be the critical core for the fibrillization and cytotoxicity. To understand the role of the GAV motif in protein amyloidogenesis, we studied the effects of the homologous peptides corresponding to the sequence of GAV motif region (residues 66–74) on α‐Syn aggregation. The result shows that these peptides can promote fibrillization of wild‐type α‐Syn and induce that of the charge‐incorporated mutants but not the GAV‐deficient α‐Syn mutant. The acceleration of α‐Syn aggregation by the homologous peptides is under a sequence‐specific manner. The interplay between the GAV peptide and the core regions in α‐Syn may accelerate the aggregation process and stabilize the fibrils. This finding provides clues for developing peptide mimics that could promote transforming the toxic oligomers or protofibrils into the inert mature fibrils.Keywords:
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Abstract To identify functional structural motifs from protein structures of unknown function becomes increasingly important in recent years due to the progress of the structural genomics initiatives. Although certain structural patterns such as the Asp‐His‐Ser catalytic triad are easy to detect because of their conserved residues and stringently constrained geometry, it is usually more challenging to detect a general structural motifs like, for example, the ββα‐metal binding motif, which has a much more variable conformation and sequence. At present, the identification of these motifs usually relies on manual procedures based on different structure and sequence analysis tools. In this study, we develop a structural alignment algorithm combining both structural and sequence information to identify the local structure motifs. We applied our method to the following examples: the ββα‐metal binding motif and the treble clef motif. The ββα‐metal binding motif plays an important role in nonspecific DNA interactions and cleavage in host defense and apoptosis. The treble clef motif is a zinc‐binding motif adaptable to diverse functions such as the binding of nucleic acid and hydrolysis of phosphodiester bonds. Our results are encouraging, indicating that we can effectively identify these structural motifs in an automatic fashion. Our method may provide a useful means for automatic functional annotation through detecting structural motifs associated with particular functions. Proteins 2006. © 2006 Wiley‐Liss, Inc.
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Abstract The shortest sequence of amino acids in protein containing functional and structural information is a “motif.” To understand myelin protein functions, we intensively searched for motifs that can be found in myelin proteins. Some myelin proteins had several different motifs or repetition of the same motif. The most abundant motif found among myelin proteins was a myristoylation motif. Bovine MAG held 11 myristoylation motifs and human myelin basic protein held as many as eight such motifs. PMP22 had the fewest myristoylation motifs, which was only one; rat PMP22 contained no such motifs. Cholesterol recognition/interaction amino‐acid consensus (CRAC) motif was not found in myelin basic protein. P2 protein of different species contained only one CRAC motif, except for P2 of horse, which had no such motifs. MAG, MOG, and P0 were very rich in CRAC, three to eight motifs per protein. The analysis of motifs in myelin proteins is expected to provide structural insight and refinement of predicted 3D models for which structures are as yet unknown. Analysis of motifs in mutant proteins associated with neurological diseases uncovered that some motifs disappeared in P0 with mutation found in neurological diseases. There are 2,500 motifs deposited in a databank, but 21 were found in myelin proteins, which is only 1% of the total known motifs. There was great variability in the number of motifs among proteins from different species. The appearance or disappearance of protein motifs after gaining point mutation in the protein related to neurological diseases was very interesting. © 2013 Wiley Periodicals, Inc.
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Abstract Nucleotide binding proteins are involved in many important cellular processes and form one of the largest protein families. Traditionally, the identification of nucleotide binding motif, such as the ATP binding P‐loop, has relied on the comparison of protein sequences, consideration of the function of each of the proteins and the identification of signature motifs within the sequence. Sometimes, it is difficult to identify nucleotide binding proteins based on sequence alignment because of increased evolutionary distances. In such cases, structural alignments can provide a better guide for comparing specific features of sequences because the overall structures of these motifs are conserved despite low sequence identity. In the present study, on the basis of bioinformatics and structural comparison of three representative protein structures of Ham1 superfamily, YjjX, YggV, and YhdE, previously identified as nucleotide binding proteins, we have identified a novel nucleotide binding motif (T/SXXXXK/R). The importance of this signature motif in binding of nucleotides was validated using site directed mutagenesis. Mutations of conserved residues of the loop either decreased or completely abolished the nucleotide binding activity of the protein. We used the conserved motif identified in the study to search for other proteins having a similar motif. Two proteins, GTP cyclohydrolase II and dephospho‐CoA pyrophosphorylase showed presence of the loop, suggesting that this nucleotide binding motif is not unique in the Ham1 superfamily, but represents a novel NTP recognition motif.
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An automatic procedure is proposed to identify, from the protein sequence database, conserved amino acid patterns (or sequence motifs) that are exclusive to a group of functionally related proteins. This procedure is applied to the PIR database and a dictionary of sequence motifs that relate to specific superfamilies constructed. The motifs have a practical relevance in identifying the membership of specific superfamilies without the need to perform sequence database searches in 20% of newly determined sequences. The sequence motifs identified represent functionally important sites on protein molecules. When multiple blocks exist in a single motif they are often close together in the 3-D structure. Furthermore, occasionally these motif blocks were found to be split by introns when the correlation with exon structures was examined.
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1. Introduction 222 2. What is an RNA motif? 222 2.1 Sequence vs . structural motifs 222 2.2 RNA structural motifs 223 2.3 RNA structural elements vs . motifs 223 2.4 Specific recognition motifs 224 2.5 Tools for identifying and classifying elements and motifs 226 3. Types of RNA structural motifs 228 3.1 Helices 228 3.2 Hairpin loops 228 3.3 Internal loops 230 3.4 Junction loops/multiloops 230 3.5 Binding motifs 232 3.5.1 Metal binding 232 3.5.2 Natural and selected aptamers 234 3.6 Tertiary interactions 234 4. Future directions 236 5. Acknowledgments 239 6. References 239 RNAs are modular biomolecules, composed largely of conserved structural subunits, or motifs. These structural motifs comprise the secondary structure of RNA and are knit together via tertiary interactions into a compact, functional, three-dimensional structure and are to be distinguished from motifs defined by sequence or function. A relatively small number of structural motifs are found repeatedly in RNA hairpin and internal loops, and are observed to be composed of a limited number of common ‘structural elements’. In addition to secondary and tertiary structure motifs, there are functional motifs specific for certain biological roles and binding motifs that serve to complex metals or other ligands. Research is continuing into the identification and classification of RNA structural motifs and is being initiated to predict motifs from sequence, to trace their phylogenetic relationships and to use them as building blocks in RNA engineering.
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The search of conserved motifs was performed in enzymes catalyzing acyladenylate formation using ATP as AMP-donor. Besides a known motif, we have found a second conserved motif. Screening the SWISS-PROT database for occurrence of the motifs have showed that both motifs are highly characteristic and occur in all proteins of this superfamily. The motifs are separated by 200-250 residues in all sequences. It may suggest that the both motifs belong to the structural unit involved in acyl adenylate formation.
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The occurrence of very similar structural motifs brought about by different parts of non homologous proteins is often indicative of a common function. Indeed, relatively small local structures can mediate binding to a common partner, be it a protein, a nucleic acid, a cofactor or a substrate. While it is relatively easy to identify short amino acid or nucleotide sequence motifs in a given set of proteins or genes, and many methods do exist for this purpose, much more challenging is the identification of common local substructures, especially if they are formed by non consecutive residues in the sequence. Here we describe a publicly available tool, able to identify common structural motifs shared by different non homologous proteins in an unsupervised mode. The motifs can be as short as three residues and need not to be contiguous or even present in the same order in the sequence. Users can submit a set of protein structures deemed or not to share a common function (e.g. they bind similar ligands, or share a common epitope). The server finds and lists structural motifs composed of three or more spatially well conserved residues shared by at least three of the submitted structures. The method uses a local structural comparison algorithm to identify subsets of similar amino acids between each pair of input protein chains and a clustering procedure to group similarities shared among different structure pairs. FunClust is fast, completely sequence independent, and does not need an a priori knowledge of the motif to be found. The output consists of a list of aligned structural matches displayed in both tabular and graphical form. We show here examples of its usefulness by searching for the largest common structural motifs in test sets of non homologous proteins and showing that the identified motifs correspond to a known common functional feature.
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