Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human)
SUBMITTER: Bishoy Wadie
PROVIDER: S-BSST668 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Viruses mimic host short linear motifs (SLiMs) to hijack and deregulate cellular functions. Studies of motif-mediated interactions therefore provide insight into virus-host dependencies, and reveal targets for therapeutic intervention. Here, we describe the pan-viral discovery of 1712 SLiM-based virus-host interactions using a phage peptidome tiling the intrinsically disordered protein regions of 229 RNA viruses. We find mimicry of host SLiMs to be a ubiquitous viral strategy, reveal novel host proteins hijacked by viruses, and identify cellular pathways frequently deregulated by viral motif mimicry. Using structural and biophysical analyses, we show that viral mimicry-based interactions have similar binding strength and bound conformations as endogenous interactions. Finally, we establish polyadenylate-binding protein 1 as a potential target for broad-spectrum antiviral agent development. Our platform enables rapid discovery of mechanisms of viral interference and the identification of potential therapeutic targets which can aid in combating future epidemics and pandemics.
| S-EPMC10132805 | biostudies-literature
Project description:Viral proteins evade host immune function by molecular mimicry, often achieved by short linear motifs (SLiMs) of three to ten consecutive amino acids (AAs). Motif mimicry tolerates mutations, evolves quickly to modify interactions with the host, and enables modular interactions with protein complexes. Host cells cannot easily coordinate changes to conserved motif recognition and binding interfaces under selective pressure to maintain critical signaling pathways. SLiMs offer potential for use in synthetic biology, such as better immunogens and therapies, but may also present biosecurity challenges. We survey viral uses of SLiMs to mimic host proteins, and information resources available for motif discovery. As the number of examples continues to grow, knowledge management tools are essential to help organize and compare new findings.
| S-EPMC7114124 | biostudies-literature
Project description:Disease mutations are traditionally thought to impair protein functionality by disrupting the folded globular structure of proteins. However, 22% of human disease mutations occur in natively unstructured segments of proteins known as intrinsically disordered regions (IDRs). This therefore implicates defective IDR functionality in various human diseases including cancer. The functionality of IDRs is partly attributable to short linear motifs (SLiMs), but it remains an open question how much defects in SLiMs contribute to human diseases. A proteome-wide comparison of the distribution of missense mutations from disease and non-disease mutation datasets revealed that, in IDRs, disease mutations are more likely to occur within SLiMs than neutral missense mutations. Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions. Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease. Furthermore, this in-depth analysis suggests that 'prostate cancer pathway' is particularly enriched for proteins with disease-related SLiMs. The contribution of mutations in SLiMs to disease may currently appear small when compared to mutations in globular domains. However, our analysis of mutations in predicted SLiMs suggests that this contribution might be more substantial. Therefore, when analysing the functional impact of mutations on proteins, SLiMs in proteins should not be neglected. Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.
| S-EPMC4306509 | biostudies-literature
Project description:BACKGROUND: Short linear protein motifs are attracting increasing attention as functionally independent sites, typically 3-10 amino acids in length that are enriched in disordered regions of proteins. Multiple methods have recently been proposed to discover over-represented motifs within a set of proteins based on simple regular expressions. Here, we extend these approaches to profile-based methods, which provide a richer motif representation. RESULTS: The profile motif discovery method MEME performed relatively poorly for motifs in disordered regions of proteins. However, when we applied evolutionary weighting to account for redundancy amongst homologous proteins, and masked out poorly conserved regions of disordered proteins, the performance of MEME is equivalent to that of regular expression methods. However, the two approaches returned different subsets within both a benchmark dataset, and a more realistic discovery dataset. CONCLUSIONS: Profile-based motif discovery methods complement regular expression based methods. Whilst profile-based methods are computationally more intensive, they are likely to discover motifs currently overlooked by regular expression methods.
| S-EPMC3534220 | biostudies-literature
Project description:There is a pressing need for in silico tools that can aid in the identification of the complete repertoire of protein binding (SLiMs, MoRFs, miniMotifs) and modification (moiety attachment/removal, isomerization, cleavage) motifs. We have created PSSMSearch, an interactive web-based tool for rapid statistical modeling, visualization, discovery and annotation of protein motif specificity determinants to discover novel motifs in a proteome-wide manner. PSSMSearch analyses proteomes for regions with significant similarity to a motif specificity determinant model built from a set of aligned motif-containing peptides. Multiple scoring methods are available to build a position-specific scoring matrix (PSSM) describing the motif specificity determinant model. This model can then be modified by a user to add prior knowledge of specificity determinants through an interactive PSSM heatmap. PSSMSearch includes a statistical framework to calculate the significance of specificity determinant model matches against a proteome of interest. PSSMSearch also includes the SLiMSearch framework's annotation, motif functional analysis and filtering tools to highlight relevant discriminatory information. Additional tools to annotate statistically significant shared keywords and GO terms, or experimental evidence of interaction with a motif-recognizing protein have been added. Finally, PSSM-based conservation metrics have been created for taxonomic range analyses. The PSSMSearch web server is available at http://slim.ucd.ie/pssmsearch/.
| S-EPMC6030969 | biostudies-literature
Project description:Short, linear motifs (SLiMs) play a critical role in many biological processes, particularly in protein-protein interactions. Overrepresentation of convergent occurrences of motifs in proteins with a common attribute (such as similar subcellular location or a shared interaction partner) provides a feasible means to discover novel occurrences computationally. The SLiMDisc (Short, Linear Motif Discovery) web server corrects for common ancestry in describing shared motifs, concentrating on the convergently evolved motifs. The server returns a listing of the most interesting motifs found within unmasked regions, ranked according to an information content-based scoring scheme. It allows interactive input masking, according to various criteria. Scoring allows for evolutionary relationships in the data sets through treatment of BLAST local alignments. Alongside this ranked list, visualizations of the results improve understanding of the context of suggested motifs, helping to identify true motifs of interest. These visualizations include alignments of motif occurrences, alignments of motifs and their homologues and a visual schematic of the top-ranked motifs. Additional options for filtering and/or re-ranking motifs further permit the user to focus on motifs with desired attributes. Returned motifs can also be compared with known SLiMs from the literature. SLiMDisc is available at: http://bioware.ucd.ie/~slimdisc/.
| S-EPMC1933137 | biostudies-literature
Project description:Short linear motifs (SLiMs) drive dynamic protein-protein interactions essential for signaling, but sequence degeneracy and low binding affinities make them difficult to identify. We harnessed unbiased systematic approaches for SLiM discovery to elucidate the regulatory network of calcineurin (CN)/PP2B, the Ca2+-activated phosphatase that recognizes LxVP and PxIxIT motifs. In vitro proteome-wide detection of CN-binding peptides, in vivo SLiM-dependent proximity labeling, and in silico modeling of motif determinants uncovered unanticipated CN interactors, including NOTCH1, which we establish as a CN substrate. Unexpectedly, CN shows SLiM-dependent proximity to centrosomal and nuclear pore complex (NPC) proteins-structures where Ca2+ signaling is largely uncharacterized. CN dephosphorylates human and yeast NPC proteins and promotes accumulation of a nuclear transport reporter, suggesting conserved NPC regulation by CN. The CN network assembled here provides a resource to investigate Ca2+ and CN signaling and demonstrates synergy between experimental and computational methods, establishing a blueprint for examining SLiM-based networks.
| S-EPMC7416808 | biostudies-literature
Project description:Viruses are obligate intracellular parasites that depend on host cellular machinery for performing even basic biological functions. One of the many ways they achieve this is through molecular mimicry, wherein the virus mimics a host sequence or structure, thereby being able to hijack the host's physiological interactions for its pathogenesis. Such adaptations are specific recognitions that often confer tissue and species-specific tropisms to the virus, and enable the virus to utilise previously existing host signalling networks, which ultimately aid in further steps of viral infection, such as entry, immune evasion and spread. A common form of sequence mimicry utilises short linear motifs (SLiMs). SLiMs are short-peptide sequences that mediate transient interactions and are major elements in host protein interaction networks. This work is aimed at providing a comprehensive review of current literature of some well-characterised SLiMs that play a role in the attachment and entry of viruses into host cells, which mimic physiological receptor-ligand interactions already present in the host. Considering recent trends in emerging diseases, further research on such motifs involved in viral entry can help in the discovery of previously unknown cellular receptors utilised by viruses, as well as help in the designing of targeted therapeutics such as vaccines or inhibitors directed towards these interactions.
| S-EPMC10072811 | biostudies-literature
Project description:Many important interactions of proteins are facilitated by short, linear motifs (SLiMs) within a protein's primary sequence. Our aim was to establish robust methods for discovering putative functional motifs. The strongest evidence for such motifs is obtained when the same motifs occur in unrelated proteins, evolving by convergence. In practise, searches for such motifs are often swamped by motifs shared in related proteins that are identical by descent. Prediction of motifs among sets of biologically related proteins, including those both with and without detectable similarity, were made using the TEIRESIAS algorithm. The number of motif occurrences arising through common evolutionary descent were normalized based on treatment of BLAST local alignments. Motifs were ranked according to a score derived from the product of the normalized number of occurrences and the information content. The method was shown to significantly outperform methods that do not discount evolutionary relatedness, when applied to known SLiMs from a subset of the eukaryotic linear motif (ELM) database. An implementation of Multiple Spanning Tree weighting outperformed two other weighting schemes, in a variety of settings.
| S-EPMC1524906 | biostudies-other
Project description:Chaperone-mediated autophagy (CMA) contributes to the lysosomal degradation of a selective subset of proteins. Selectivity lies in the chaperone heat shock cognate 71 kDa protein (HSC70) recognizing a pentapeptide motif (KFERQ-like motif) in the protein sequence essential for subsequent targeting and degradation of CMA substrates in lysosomes. Interest in CMA is growing due to its recently identified regulatory roles in metabolism, differentiation, cell cycle, and its malfunctioning in aging and conditions such as cancer, neurodegeneration, or diabetes. Identification of the subset of the proteome amenable to CMA degradation could further expand our understanding of the pathophysiological relevance of this form of autophagy. To that effect, we have performed an in silico screen for KFERQ-like motifs across proteomes of several species. We have found that KFERQ-like motifs are more frequently located in solvent-exposed regions of proteins, and that the position of acidic and hydrophobic residues in the motif plays the most important role in motif construction. Cross-species comparison of proteomes revealed higher motif conservation in CMA-proficient species. The tools developed in this work have also allowed us to analyze the enrichment of motif-containing proteins in biological processes on an unprecedented scale and discover a previously unknown association between the type and combination of KFERQ-like motifs in proteins and their participation in specific biological processes. To facilitate further analysis by the scientific community, we have developed a free web-based resource (KFERQ finder) for direct identification of KFERQ-like motifs in any protein sequence. This resource will contribute to accelerating understanding of the physiological relevance of CMA.
| S-EPMC6561683 | biostudies-literature