A computational strategy for the prediction of functional linear peptide motifs in proteins

Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions

Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.

Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.

Comparative Analysis of Structural Features in SLiMs from Eukaryotes, Bacteria, and Viruses with Importance for Host-Pathogen Interactions

Protein-protein interactions drive functions in eukaryotes that can be described by short linear motifs (SLiMs). Conservation of SLiMs help illuminate functional SLiMs in eukaryotic protein families. However, the simplicity of eukaryotic SLiMs makes them appear by chance due to mutational processes not only in eukaryotes but also in pathogenic bacteria and viruses. Further, functional eukaryotic SLiMs are often found in disordered regions. Although proteomes from pathogenic bacteria and viruses have less disorder than eukaryotic proteomes, their proteins can successfully mimic eukaryotic SLiMs and disrupt host cellular function. Identifying important SLiMs in pathogens is difficult but essential for understanding potential host-pathogen interactions. We performed a comparative analysis of structural features for experimentally verified SLiMs from the Eukaryotic Linear Motif (ELM) database across viruses, bacteria, and eukaryotes. Our results revealed that many viral SLiMs and specific motifs found across viruses and eukaryotes, such as some glycosylation motifs, have less disorder. Analyzing the disorder and coil properties of equivalent SLiMs from pathogens and eukaryotes revealed that some motifs are more structured in pathogens than their eukaryotic counterparts and vice versa. These results support a varying mechanism of interaction between pathogens and their eukaryotic hosts for some of the same motifs.

Resources to Discover and Use Short Linear Motifs in Viral Proteins

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.

The subcellular localization of bHLH transcription factor TCF4 is mediated by multiple nuclear localization and nuclear export signals

Transcription factor 4 (TCF4) is a class I basic helix-loop-helix (bHLH) transcription factor which regulates the neurogenesis and specialization of cells. TCF4 also plays an important role in the development and functioning of the immune system. Additionally, TCF4 regulates the development of Sertoli cells and pontine nucleus neurons, myogenesis, melanogenesis and epithelial-mesenchymal transition. The ability of transcription factors to fulfil their function often depends on their intracellular trafficking between the nucleus and cytoplasm of the cell. The trafficking is regulated by specific sequences, i.e. the nuclear localization signal (NLS) and the nuclear export signal (NES). We performed research on the TCF4 trafficking regulating sequences by mapping and detailed characterization of motifs potentially acting as the NLS or NES. We demonstrate that the bHLH domain of TCF4 contains an NLS that overlaps two NESs. The results of in silico analyses show high conservation of the sequences, especially in the area of the NLS and NESs. This high conservation is not only between mouse and human TCF4, but also between TCF4 and other mammalian E proteins, indicating the importance of these sequences for the functioning of bHLH class I transcription factors.

Multiple sequences orchestrate subcellular trafficking of neuronal PAS domain-containing protein 4 (NPAS4)

Neuronal Per-Arnt-Sim (PAS) domain-containing protein 4 (NPAS4) is a basic helix-loop-helix (bHLH)-PAS transcription factor first discovered in neurons in the neuronal layer of the mammalian hippocampus and later discovered in pancreatic β-cells. NPAS4 has been proposed as a therapeutic target not only for depression and neurodegenerative diseases associated with synaptic dysfunction but also for type 2 diabetes and pancreas transplantation. The ability of bHLH-PAS proteins to fulfil their function depends on their intracellular trafficking, which is regulated by specific sequences, i.e. the nuclear localization signal (NLS) and the nuclear export signal (NES). However, until now, no study examining the subcellular localization signals of NPAS4 has been published. We show here that Rattus norvegicus NPAS4 was not uniformly localized in the nuclei of COS-7 and N2a cells 24 h after transfection. Additionally, cytoplasmic localization of NPAS4 was leptomycin B-sensitive. We demonstrate that NPAS4 possesses a unique arrangement of localization signals. Its bHLH domain contains an overlapping NLS and NES. We observed that its PAS-2 domain contains an NLS, an NES, and a second, proximally located, putative NLS. Moreover, the C terminus of NPAS4 contains two active NESs that overlap with a putative NLS. Our data indicate that glucose concentration could be one of the factors influencing NPAS4 localization. The presence of multiple localization signals and the differentiated localization of NPAS4 suggest a precise, multifactor-dependent regulation of NPAS4 trafficking, potentially crucial for its ability to act as a cellular stress sensor and transcription factor.

Observation selection bias in contact prediction and its implications for structural bioinformatics

Next Generation Sequencing is dramatically increasing the number of known protein sequences, with related experimentally determined protein structures lagging behind. Structural bioinformatics is attempting to close this gap by developing approaches that predict structure-level characteristics for uncharacterized protein sequences, with most of the developed methods relying heavily on evolutionary information collected from homologous sequences. Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot. Structural bioinformatics methods that were developed this way are thus likely to have over-estimated performances; we demonstrate this for two contact prediction methods, where performances drop up to 60% when taking into account a more realistic amount of evolutionary information. We provide a bias-free dataset for the validation for contact prediction methods called NOUMENON.

Design of hyperthermophilic lipase chimeras by key motif-directed recombination

Recombination of diverse natural evolved domains within a superfamily offers greater opportunity for enzyme function leaps. How to recombine protein modules from distant parents with less disruption in cross-interfaces is a challenging issue. Here, we identified the existence of a key motif, the sequence VVSVN(D)YR, within a structural motif ψ loop in the α/β-hydrolase fold superfamily, by using a MEME server and the PROMOTIF program. To obtain thermostable lipase-like enzymes, two chimeras were engineered at the key motif regions through recombination of domains from a mesophilic lipase and a hyperthermophilic esterase/peptidase with amino acid identity less than 21 %. The chimeras retained the desirable substrate preference of their mesophilic parent and exhibited more than 100-fold increased thermostability at 50 °C. Through site-directed mutation, we further improved activity of the chimera by 4.6-fold. The recombination strategy presented here enables the creation of novel catalysts.

Identification of enriched PTM crosstalk motifs from large-scale experimental data sets

Post-translational modifications (PTMs) play an important role in the regulation of protein function. Mass spectrometry based proteomics experiments nowadays identify tens of thousands of PTMs in a single experiment. A wealth of data has therefore become publically available. Evidently the biological function of each PTM is the key question to be addressed; however, such analyses focus primarily on single PTM events. This ignores the fact that PTMs may act in concert in the regulation of protein function, a process termed PTM crosstalk. Relatively little is known on the frequency and functional relevance of crosstalk between PTM sites. In a bioinformatics approach, we extracted PTMs occurring in proximity in the protein sequence from publically available databases. These PTMs and their flanking sequences were subjected to stringent motif searches, including a scoring for evolutionary conservation. Our unprejudiced approach was able to detect a respectable set of motifs, of which about half were described previously. Among these we could add many new proteins harboring these motifs. We extracted also several novel motifs, which through their widespread appearance and high conservation may pinpoint at previously nonannotated concerted PTM actions. By employing network analyses on these proteins, we propose putative functional roles for these novel motifs with two PTM sites in close proximity.

Expression, purification, and structural analysis of intracellular C-termini from metabotropic glutamate receptors

Glutamate is the most important excitatory neurotransmitter in the mammalian central nervous system (CNS). Metabotropic glutamate receptors (mGluRs) are G-protein-coupled receptors (GPCRs) that guide several intracellular signal cascades thereby controlling multiple physiological tasks, such as neuronal excitability, learning, and memory. Consequently, these receptors are discussed in the context of several CNS-associated diseases, including addiction for drugs, Alzheimer's disease, Fragile X syndrome, night blindness, or schizophrenia. Although increasing structural information is available for the extracellular and transmembrane domains of GPCRs, data describing the nature of intracellular receptor domains are largely missing. Indeed, in all available crystal structures of neurotransmitter receptors, their intracellular domains were omitted. Most intracellular mGluR C-termini are alternatively spliced and contain multiple binding sites for interacting proteins. Therefore, analyzing their structure can identify molecular mechanisms of receptor regulation. Recently, we analyzed the conformation of the intracellular C-termini of mGluR6, mGluR7a, and mGluR8a. Here, we describe an array of biochemical, biophysical, and computational techniques suited to elucidate the nature of these highly interesting receptor domains.

SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions

Large portions of higher eukaryotic proteomes are intrinsically disordered, and abundant evidence suggests that these unstructured regions of proteins are rich in regulatory interaction interfaces. A major class of disordered interaction interfaces are the compact and degenerate modules known as short linear motifs (SLiMs). As a result of the difficulties associated with the experimental identification and validation of SLiMs, our understanding of these modules is limited, advocating the use of computational methods to focus experimental discovery. This article evaluates the use of evolutionary conservation as a discriminatory technique for motif discovery. A statistical framework is introduced to assess the significance of relatively conserved residues, quantifying the likelihood a residue will have a particular level of conservation given the conservation of the surrounding residues. The framework is expanded to assess the significance of groupings of conserved residues, a metric that forms the basis of SLiMPrints (short linear motif fingerprints), a de novo motif discovery tool. SLiMPrints identifies relatively overconstrained proximal groupings of residues within intrinsically disordered regions, indicative of putatively functional motifs. Finally, the human proteome is analysed to create a set of highly conserved putative motif instances, including a novel site on translation initiation factor eIF2A that may regulate translation through binding of eIF4E.

iELM--a web server to explore short linear motif-mediated interactions

The recent expansion in our knowledge of protein–protein interactions (PPIs) has allowed the annotation and prediction of hundreds of thousands of interactions. However, the function of many of these interactions remains elusive. The interactions of Eukaryotic Linear Motif (iELM) web server provides a resource for predicting the function and positional interface for a subset of interactions mediated by short linear motifs (SLiMs). The iELM prediction algorithm is based on the annotated SLiM classes from the Eukaryotic Linear Motif (ELM) resource and allows users to explore both annotated and user-generated PPI networks for SLiM-mediated interactions. By incorporating the annotated information from the ELM resource, iELM provides functional details of PPIs. This can be used in proteomic analysis, for example, to infer whether an interaction promotes complex formation or degradation. Furthermore, details of the molecular interface of the SLiM-mediated interactions are also predicted. This information is displayed in a fully searchable table, as well as graphically with the modular architecture of the participating proteins extracted from the UniProt and Phospho.ELM resources. A network figure is also presented to aid the interpretation of results. The iELM server supports single protein queries as well as large-scale proteomic submissions and is freely available at http://i.elm.eu.org.

ELM--the database of eukaryotic linear motifs

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

Attributes of short linear motifs

Traditionally, protein-protein interactions were thought to be mediated by large, structured domains. However, it has become clear that the interactome comprises a wide range of binding interfaces with varying degrees of flexibility, ranging from rigid globular domains to disordered regions that natively lack structure. Enrichment for disorder in highly connected hub proteins and its correlation with organism complexity hint at the functional importance of disordered regions. Nevertheless, they have not yet been extensively characterised. Shifting the attention from globular domains to disordered regions of the proteome might bring us closer to elucidating the dense and complex connectivity of the interactome. An important class of disordered interfaces are the compact mono-partite, short linear motifs (SLiMs, or eukaryotic linear motifs (ELMs)). They are evolutionarily plastic and interact with relatively low affinity due to the limited number of residues that make direct contact with the binding partner. These features confer to SLiMs the ability to evolve convergently and mediate transient interactions, which is imperative to network evolution and to maintain robust cell signalling, respectively. The ability to discriminate biologically relevant SLiMs by means of different attributes will improve our understanding of the complexity of the interactome and aid development of bioinformatics tools for motif discovery. In this paper, the curated instances currently available in the Eukaryotic Linear Motif (ELM) database are analysed to provide a clear overview of the defining attributes of SLiMs. These analyses suggest that functional SLiMs have higher levels of conservation than their surrounding residues, frequently evolve convergently, preferentially occur in disordered regions and often form a secondary structure when bound to their interaction partner. These results advocate searching for small groupings of residues in disordered regions with higher relative conservation and a propensity to form the secondary structure. Finally, the most interesting conclusions are examined in regard to their functional consequences.

Interactome-wide prediction of short, disordered protein interaction motifs in humans

Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying in silico SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (). In addition to True Positive results for at least 25 different known SLiMs, a striking number of "off-target" proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.

In silico protein motif discovery and structural analysis

A wealth of in silico tools is available for protein motif discovery and structural analysis. The aim of this chapter is to collect some of the most common and useful tools and to guide the biologist in their use. A detailed explanation is provided for the use of Distill, a suite of web servers for the prediction of protein structural features and the prediction of full-atom 3D models from a protein sequence. Besides this, we also provide pointers to many other tools available for motif discovery and secondary and tertiary structure prediction from a primary amino acid sequence. The prediction of protein intrinsic disorder and the prediction of functional sites and SLiMs are also briefly discussed. Given that user queries vary greatly in size, scope and character, the trade-offs in speed, accuracy and scale need to be considered when choosing which methods to adopt.

Large-scale discovery and characterization of protein regulatory motifs in eukaryotes

The increasing ability to generate large-scale, quantitative proteomic data has brought with it the challenge of analyzing such data to discover the sequence elements that underlie systems-level protein behavior. Here we show that short, linear protein motifs can be efficiently recovered from proteome-scale datasets such as sub-cellular localization, molecular function, half-life, and protein abundance data using an information theoretic approach. Using this approach, we have identified many known protein motifs, such as phosphorylation sites and localization signals, and discovered a large number of candidate elements. We estimate that ∼80% of these are novel predictions in that they do not match a known motif in both sequence and biological context, suggesting that post-translational regulation of protein behavior is still largely unexplored. These predicted motifs, many of which display preferential association with specific biological pathways and non-random positioning in the linear protein sequence, provide focused hypotheses for experimental validation.

Two isoforms of the protein kinase pUL97 of human cytomegalovirus are differentially regulated in their nuclear translocation

The pUL97 protein kinase encoded by human cytomegalovirus is a multifunctional determinant of the efficiency of viral replication and phosphorylates viral as well as cellular substrate proteins. Here, we report that pUL97 is expressed in two isoforms with molecular masses of approximately 90 and 100 kDa. ORF UL97 comprises an unusual coding strategy in that five in-frame ATG start codons are contained within the N-terminal 157 aa. Site-directed mutagenesis, transient expression of point and deletion mutants and proteomic analyses accumulated evidence that the formation of the large and small isoforms result from alternative initiation of translation, with the start points being at amino acids 1 and 74, respectively. In vitro kinase assays demonstrated that catalytic activity, in terms of autophosphorylation and histone substrate phosphorylation, was indistinguishable for the two isoforms. An analysis of the intracellular distribution of pUL97 by confocal laser-scanning microscopy demonstrated that both isoforms have a pronounced nuclear localization. Surprisingly, mapping experiments performed to identify the nuclear localization signal (NLS) of pUL97 strongly suggest that the mechanism of nuclear transport is distinct for the two isoforms. While the extreme N terminus (large isoform) comprises a highly efficient, bipartite NLS (amino acids 6-35), a second sequence apparently conferring a less efficient mode of nuclear translocation was identified downstream of amino acid 74 (small and large isoforms). Taken together, the findings argue for a complex mechanism of nuclear translocation for pUL97 which might be linked with fine-regulatory differences between the two isoforms.

Novel mode of phosphorylation-triggered reorganization of the nuclear lamina during nuclear egress of human cytomegalovirus

The nucleocytoplasmic egress of viral capsids is a rate-limiting step in the replication of the human cytomegalovirus (HCMV). As reported recently, an HCMV-specific nuclear egress complex is composed of viral and cellular proteins, in particular protein kinases with the capacity to induce destabilization of the nuclear lamina. Viral protein kinase pUL97 and cellular protein kinase C (PKC) play important roles by phosphorylating several types of nuclear lamins. Using pUL97 mutants, we show that the lamin-phosphorylating activity of pUL97 is associated with a reorganization of nuclear lamin A/C. Either pUL97 or PKC has the potential to induce distinct punctate lamina-depleted areas at the periphery of the nuclear envelope, which were detectable in transiently transfected and HCMV-infected cells. Using recombinant HCMV, which produces green fluorescent protein-labeled viral capsids, the direct transition of viral capsids through these areas could be visualized. This process was sensitive to an inhibitor of pUL97/PKC activity. The pUL97-mediated phosphorylation of lamin A/C at Ser(22) generated a novel binding motif for the peptidyl-prolyl cis/trans-isomerase Pin1. In HCMV-infected fibroblasts, the physiological localization of Pin1 was altered, leading to recruitment of Pin1 to viral replication centers and to the nuclear lamina. The local increase in Pin1 peptidyl-prolyl cis/trans-isomerase activity may promote conformational modulation of lamins. Thus, we postulate a novel phosphorylation-triggered mechanism for the reorganization of the nuclear lamina in HCMV-infected cells.

ELM: the status of the 2010 eukaryotic linear motif resource

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a 'Bar Code' format, which also displays known instances from homologous proteins through a novel 'Instance Mapper' protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.

A structure filter for the Eukaryotic Linear Motif Resource

Background

Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality.

Results

Current methods assessing motif accessibility do not consider much of the information available, either predicting accessibility from primary sequence or regarding any motif occurring in a globular region as low confidence. We present a method considering accessibility and secondary structural context derived from experimentally solved protein structures to rectify this situation. Putatively functional motif occurrences are mapped onto a representative domain, given that a high quality reference SCOP domain structure is available for the protein itself or a close relative. Candidate motifs can then be scored for solvent-accessibility and secondary structure context. The scores are calibrated on a benchmark set of experimentally verified motif instances compared with a set of random matches. A combined score yields 3-fold enrichment for functional motifs assigned to high confidence classifications and 2.5-fold enrichment for random motifs assigned to low confidence classifications. The structure filter is implemented as a pipeline with both a graphical interface via the ELM resource and through a Web Service protocol.

Conclusion

New occurrences of known linear motifs require experimental validation as the bioinformatics tools currently have limited reliability. The ELM structure filter will aid users assessing candidate motifs presenting in globular structural regions. Most importantly, it will help users to decide whether to expend their valuable time and resources on experimental testing of interesting motif candidates.

Cell regulation: determined to signal discrete cooperation

Do kinases cascade? How well is cell regulation understood? What are the best ways to model regulatory systems? Attempts to answer such questions can have bearings on the way in which research is conducted. Fortunately there are recurring themes in regulatory processes from many different cellular contexts, which might provide useful guidance. Three principles seem to be almost universal: regulatory interactions are cooperative; regulatory decisions are made by large dynamic protein complexes; and regulation is intricately networked. A fourth principle, although not universal, is remarkably common: regulatory proteins are actively placed where they are needed. Here, I argue that the true nature of cell signalling and our perceptions of it are in a state of discord. This raises the question: Are our misconceptions detrimental to progress in biomedical science?

Evidence for the concerted evolution between short linear protein motifs and their flanking regions

BACKGROUND

Linear motifs are short modules of protein sequences that play a crucial role in mediating and regulating many protein-protein interactions. The function of linear motifs strongly depends on the context, e.g. functional instances mainly occur inside flexible regions that are accessible for interaction. Sometimes linear motifs appear as isolated islands of conservation in multiple sequence alignments. However, they also occur in larger blocks of sequence conservation, suggesting an active role for the neighbouring amino acids.

RESULTS

The evolution of regions flanking 116 functional linear motif instances was studied. The conservation of the amino acid sequence and order/disorder tendency of those regions was related to presence/absence of the instance. For the majority of the analysed instances, the pairs of sequences conserving the linear motif were also observed to maintain a similar local structural tendency and/or to have higher local sequence conservation when compared to pairs of sequences where one is missing the linear motif. Furthermore, those instances have a higher chance to co-evolve with the neighbouring residues in comparison to the distant ones. Those findings are supported by examples where the regulation of the linear motif-mediated interaction has been shown to depend on the modifications (e.g. phosphorylation) at neighbouring positions or is thought to benefit from the binding versatility of disordered regions.

CONCLUSION

The results suggest that flanking regions are relevant for linear motif-mediated interactions, both at the structural and sequence level. More interestingly, they indicate that the prediction of linear motif instances can be enriched with contextual information by performing a sequence analysis similar to the one presented here. This can facilitate the understanding of the role of these predicted instances in determining the protein function inside the broader context of the cellular network where they arise.

seeMotif: exploring and visualizing sequence motifs in 3D structures

Sequence motifs are important in the study of molecular biology. Motif discovery tools efficiently deliver many function related signatures of proteins and largely facilitate sequence annotation. As increasing numbers of motifs are detected experimentally or predicted computationally, characterizing the functional roles of motifs and identifying the potential synergetic relationships between them are important next steps. A good way to investigate novel motifs is to utilize the abundant 3D structures that have also been accumulated at an astounding rate in recent years. This article reports the development of the web service seeMotif, which provides users with an interactive interface for visualizing sequence motifs on protein structures from the Protein Data Bank (PDB). Researchers can quickly see the locations and conformation of multiple motifs among a number of related structures simultaneously. Considering the fact that PDB sequences are usually shorter than those in sequence databases and/or may have missing residues, seeMotif has two complementary approaches for selecting structures and mapping motifs to protein chains in structures. As more and more structures belonging to previously uncharacterized protein families become available, combining sequence and structure information gives good opportunities to facilitate understanding of protein functions in large-scale genome projects. Available at: http://seemotif.csie.ntu.edu.tw,http://seemotif.ee.ncku.edu.tw or http://seemotif.csbb.ntu.edu.tw.

Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs

Background

Host protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology favors excessive amounts of virus production in a stressed host cell network. Short linear peptide motifs common to both virus and host provide the basis for host network modification.

Methods

We focused our host-pathogen study on the binding and competing interactions of HIV-1 and human proteins. We showed that peptide motifs conserved across 70% of HIV-1 subtype B and C samples occurred in similar positions on HIV-1 proteins, and we documented protein domains that interact with these conserved motifs. We predicted which human proteins may be targeted by HIV-1 by taking pairs of human proteins that may interact via a motif conserved in HIV-1 and the corresponding interacting protein domain.

Results

Our predictions were enriched with host proteins known to interact with HIV-1 proteins ENV, NEF, and TAT (p-value < 4.26E-21). Cellular pathways statistically enriched for our predictions include the T cell receptor signaling, natural killer cell mediated cytotoxicity, cell cycle, and apoptosis pathways. Gene Ontology molecular function level 5 categories enriched with both predicted and confirmed HIV-1 targeted proteins included categories associated with phosphorylation events and adenyl ribonucleotide binding.

Conclusion

A list of host proteins highly enriched with those targeted by HIV-1 proteins can be obtained by searching for host protein motifs along virus protein sequences. The resulting set of host proteins predicted to be targeted by virus proteins will become more accurate with better annotations of motifs and domains. Nevertheless, our study validates the role of linear binding motifs shared by virus and host proteins as an important part of the crosstalk between virus and host.

Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery

MOTIVATION

Short linear motifs (SLiMs) are important mediators of protein-protein interactions. Their short and degenerate nature presents a challenge for computational discovery. We sought to improve SLiM discovery by incorporating evolutionary information, since SLiMs are more conserved than surrounding residues.

RESULTS

We have developed a new method that assesses the evolutionary signal of a residue in its sequence and structural context. Under-conserved residues are masked out prior to SLiM discovery, allowing incorporation into the existing statistical model employed by SLiMFinder. The method shows considerable robustness in terms of both the conservation score used for individual residues and the size of the sequence neighbourhood. Optimal parameters significantly improve return of known functional motifs from benchmarking data, raising the return of significant validated SLiMs from typical human interaction datasets from 20% to 60%, while retaining the high level of stringency needed for application to real biological data. The success of this regime indicates that it could be of general benefit to computational annotation and prediction of protein function at the sequence level.

AVAILABILITY

All data and tools in this article are available at http://bioware.ucd.ie/~slimdisc/slimfinder/conmasking/.

The conservation pattern of short linear motifs is highly correlated with the function of interacting protein domains

Background

Many well-represented domains recognize primary sequences usually less than 10 amino acids in length, called Short Linear Motifs (SLiMs). Accurate prediction of SLiMs has been difficult because they are short (often < 10 amino acids) and highly degenerate. In this study, we combined scoring matrixes derived from peptide library and conservation analysis to identify protein classes enriched of functional SLiMs recognized by SH2, SH3, PDZ and S/T kinase domains.

Results

Our combined approach revealed that SLiMs are highly conserved in proteins from functional classes that are known to interact with a specific domain, but that they are not conserved in most other protein groups. We found that SLiMs recognized by SH2 domains were highly conserved in receptor kinases/phosphatases, adaptor molecules, and tyrosine kinases/phosphatases, that SLiMs recognized by SH3 domains were highly conserved in cytoskeletal and cytoskeletal-associated proteins, that SLiMs recognized by PDZ domains were highly conserved in membrane proteins such as channels and receptors, and that SLiMs recognized by S/T kinase domains were highly conserved in adaptor molecules, S/T kinases/phosphatases, and proteins involved in transcription or cell cycle control. We studied Tyr-SLiMs recognized by SH2 domains in more detail, and found that SH2-recognized Tyr-SLiMs on the cytoplasmic side of membrane proteins are more highly conserved than those on the extra-cellular side. Also, we found that SH2-recognized Tyr-SLiMs that are associated with SH3 motifs and a tyrosine kinase phosphorylation motif are more highly conserved.

Conclusion

The interactome of protein domains is reflected by the evolutionary conservation of SLiMs recognized by these domains. Combining scoring matrixes derived from peptide libraries and conservation analysis, we would be able to find those protein groups that are more likely to interact with specific domains.

A tree-based conservation scoring method for short linear motifs in multiple alignments of protein sequences

Background

The structure of many eukaryotic cell regulatory proteins is highly modular. They are assembled from globular domains, segments of natively disordered polypeptides and short linear motifs. The latter are involved in protein interactions and formation of regulatory complexes. The function of such proteins, which may be difficult to define, is the aggregate of the subfunctions of the modules. It is therefore desirable to efficiently predict linear motifs with some degree of accuracy, yet sequence database searches return results that are not significant.

Results

We have developed a method for scoring the conservation of linear motif instances. It requires only primary sequence-derived information (e.g. multiple alignment and sequence tree) and takes into account the degenerate nature of linear motif patterns. On our benchmarking, the method accurately scores 86% of the known positive instances, while distinguishing them from random matches in 78% of the cases. The conservation score is implemented as a real time application designed to be integrated into other tools. It is currently accessible via a Web Service or through a graphical interface.

Conclusion

The conservation score improves the prediction of linear motifs, by discarding those matches that are unlikely to be functional because they have not been conserved during the evolution of the protein sequences. It is especially useful for instances in non-structured regions of the proteins, where a domain masking filtering strategy is not applicable.

A new protein linear motif benchmark for multiple sequence alignment software

BACKGROUND

Linear motifs (LMs) are abundant short regulatory sites used for modulating the functions of many eukaryotic proteins. They play important roles in post-translational modification, cell compartment targeting, docking sites for regulatory complex assembly and protein processing and cleavage. Methods for LM detection are now being developed that are strongly dependent on scores for motif conservation in homologous proteins. However, most LMs are found in natively disordered polypeptide segments that evolve rapidly, unhindered by structural constraints on the sequence. These regions of modular proteins are difficult to align using classical multiple sequence alignment programs that are specifically optimised to align the globular domains. As a consequence, poor motif alignment quality is hindering efforts to detect new LMs.

RESULTS

We have developed a new benchmark, as part of the BAliBASE suite, designed to assess the ability of standard multiple alignment methods to detect and align LMs. The reference alignments are organised into different test sets representing real alignment problems and contain examples of experimentally verified functional motifs, extracted from the Eukaryotic Linear Motif (ELM) database. The benchmark has been used to evaluate and compare a number of multiple alignment programs. With distantly related proteins, the worst alignment program correctly aligns 48% of LMs compared to 73% for the best program. However, the performance of all the programs is adversely affected by the introduction of other sequences containing false positive motifs. The ranking of the alignment programs based on LM alignment quality is similar to that observed when considering full-length protein alignments, however little correlation was observed between LM and overall alignment quality for individual alignment test cases.

CONCLUSION

We have shown that none of the programs currently available is capable of reliably aligning LMs in distantly related sequences and we have highlighted a number of specific problems. The results of the tests suggest possible ways to improve program accuracy for difficult, divergent sequences.