Protein Short Linear Motifs Research Articles

Multiple, short protein binding motifs in ORC1 and CDC6 control the initiation of DNA replication

The initiation of DNA replication involves cell cycle-dependent assembly and disassembly of protein complexes, including the origin recognition complex (ORC) and CDC6 AAA+ ATPases. We report that multiple short linear protein motifs (SLiMs) within intrinsically disordered regions (IDRs) in ORC1 and CDC6 mediate cyclin-CDK-dependent and independent protein-protein interactions, conditional on the cell cycle phase. A domain within the ORC1 IDR is required for interaction between the ORC1 and CDC6 AAA+ domains in G1, whereas the same domain prevents CDC6-ORC1 interaction during mitosis. Then, during late G1, this domain facilitates ORC1 destruction by a SKP2-cyclin A-CDK2-dependent mechanism. During G1, the CDC6 Cy motif cooperates with cyclin E-CDK2 to promote ORC1-CDC6 interactions. The CDC6 IDR regulates self-interaction by ORC1, thereby controlling ORC1 protein levels. Protein phosphatase 1 binds directly to a SLiM in the ORC1 IDR, causing ORC1 de-phosphorylation upon mitotic exit, increasing ORC1 protein, and promoting pre-RC assembly.

Interactomic affinity profiling by holdup assay: Acetylation and distal residues impact the PDZome-binding specificity of PTEN phosphatase.

Protein domains often recognize short linear protein motifs composed of a core conserved consensus sequence surrounded by less critical, modulatory positions. PTEN, a lipid phosphatase involved in phosphatidylinositol 3-kinase (PI3K) pathway, contains such a short motif located at the extreme C-terminus capable to recognize PDZ domains. It has been shown that the acetylation of this motif could modulate the interaction with several PDZ domains. Here we used an accurate experimental approach combining high-throughput holdup chromatographic assay and competitive fluorescence polarization technique to measure quantitative binding affinity profiles of the PDZ domain-binding motif (PBM) of PTEN. We substantially extended the previous knowledge towards the 266 known human PDZ domains, generating the full PDZome-binding profile of the PTEN PBM. We confirmed that inclusion of N-terminal flanking residues, acetylation or mutation of a lysine at a modulatory position significantly altered the PDZome-binding profile. A numerical specificity index is also introduced as an attempt to quantify the specificity of a given PBM over the complete PDZome. Our results highlight the impact of modulatory residues and post-translational modifications on PBM interactomes and their specificity.

HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons.

Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery. We have developed HH-MOTiF, a web-based method for SLiM discovery in sets of mainly unrelated proteins. HH-MOTiF makes use of evolutionary information by creating Hidden Markov Models (HMMs) for each input sequence and its closely related orthologs. HMMs are compared against each other to retrieve short stretches of homology that represent potential SLiMs. These are transformed to hierarchical structures, which we refer to as motif trees, for further processing and evaluation. Our approach allows us to identify degenerate SLiMs, while still maintaining a reasonably high precision. When considering a balanced measure for recall and precision, HH-MOTiF performs better on test data compared to other SLiM discovery methods. HH-MOTiF is freely available as a web-server at http://hh-motif.biochem.mpg.de.

HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons.

Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery. We have developed HH-MOTiF, a web-based method for SLiM discovery in sets of mainly unrelated proteins. HH-MOTiF makes use of evolutionary information by creating Hidden Markov Models (HMMs) for each input sequence and its closely related orthologs. HMMs are compared against each other to retrieve short stretches of homology that represent potential SLiMs. These are transformed to hierarchical structures, which we refer to as motif trees, for further processing and evaluation. Our approach allows us to identify degenerate SLiMs, while still maintaining a reasonably high precision. When considering a balanced measure for recall and precision, HH-MOTiF performs better on test data compared to other SLiM discovery methods. HH-MOTiF is freely available as a web-server at http://hh-motif.biochem.mpg.de.

Short linear motifs - ex nihilo evolution of protein regulation.

Short sequence motifs are ubiquitous across the three major types of biomolecules: hundreds of classes and thousands of instances of DNA regulatory elements, RNA motifs and protein short linear motifs (SLiMs) have been characterised. The increase in complexity of transcriptional, post-transcriptional and post-translational regulation in higher Eukaryotes has coincided with a significant expansion of motif use. But how did the eukaryotic cell acquire such a vast repertoire of motifs? In this review, we curate the available literature on protein motif evolution and discuss the evidence that suggests SLiMs can be acquired by mutations, insertions and deletions in disordered regions. We propose a mechanism of ex nihilo SLiM evolution – the evolution of a novel SLiM from “nothing” – adding a functional module to a previously non-functional region of protein sequence. In our model, hundreds of motif-binding domains in higher eukaryotic proteins connect simple motif specificities with useful functions to create a large functional motif space. Accessible peptides that match the specificity of these motif-binding domains are continuously created and destroyed by mutations in rapidly evolving disordered regions, creating a dynamic supply of new interactions that may have advantageous phenotypic novelty. This provides a reservoir of diversity to modify existing interaction networks. Evolutionary pressures will act on these motifs to retain beneficial instances. However, most will be lost on an evolutionary timescale as negative selection and genetic drift act on deleterious and neutral motifs respectively. In light of the parallels between the presented model and the evolution of motifs in the regulatory segments of genes and (pre-)mRNAs, we suggest our understanding of regulatory networks would benefit from the creation of a shared model describing the evolution of transcriptional, post-transcriptional and post-translational regulation.

Design and evaluation of antimalarial peptides derived from prediction of short linear motifs in proteins related to erythrocyte invasion.

The purpose of this study was to investigate the blood stage of the malaria causing parasite, Plasmodium falciparum, to predict potential protein interactions between the parasite merozoite and the host erythrocyte and design peptides that could interrupt these predicted interactions. We screened the P. falciparum and human proteomes for computationally predicted short linear motifs (SLiMs) in cytoplasmic portions of transmembrane proteins that could play roles in the invasion of the erythrocyte by the merozoite, an essential step in malarial pathogenesis. We tested thirteen peptides predicted to contain SLiMs, twelve of them palmitoylated to enhance membrane targeting, and found three that blocked parasite growth in culture by inhibiting the initiation of new infections in erythrocytes. Scrambled peptides for two of the most promising peptides suggested that their activity may be reflective of amino acid properties, in particular, positive charge. However, one peptide showed effects which were stronger than those of scrambled peptides. This was derived from human red blood cell glycophorin-B. We concluded that proteome-wide computational screening of the intracellular regions of both host and pathogen adhesion proteins provides potential lead peptides for the development of anti-malarial compounds.

Versatile communication strategies among tandem WW domain repeats.

Interactions mediated by short linear motifs in proteins play major roles in regulation of cellular homeostasis since their transient nature allows for easy modulation. We are still far from a full understanding and appreciation of the complex regulation patterns that can be, and are, achieved by this type of interaction. The fact that many linear-motif-binding domains occur in tandem repeats in proteins indicates that their mutual communication is used extensively to obtain complex integration of information toward regulatory decisions. This review is an attempt to overview, and classify, different ways by which two and more tandem repeats cooperate in binding to their targets, in the well-characterized family of WW domains and their corresponding polyproline ligands.

Computational prediction of short linear motifs from protein sequences.

Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3-15 amino acids in length and have 2-5 defined positions, making them highly likely to occur by chance and extremely difficult to identify. Nevertheless, our knowledge of SLiMs and capacity to predict them from protein sequence data using computational methods has advanced dramatically over the past decade. By considering the biological, structural, and evolutionary context of SLiM occurrences, it is possible to differentiate functional instances from chance matches in many cases and to identify new regions of proteins that have the features consistent with a SLiM-mediated interaction. Their simplicity also makes SLiMs evolutionarily labile and prone to independent origins on different sequence backgrounds through convergent evolution, which can be exploited for predicting novel SLiMs in proteins that share a function or interaction partner. In this review, we explore our current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences. Rather than focusing on specific SLiM prediction tools, we provide an overview of the methods available and concentrate on principles that should continue to be paramount even in the light of future developments. We consider the relative merits of using regular expressions or profiles for SLiM discovery and discuss the main considerations for both predicting new instances of known SLiMs, and de novo prediction of novel SLiMs. In particular, we highlight the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.

Profile-based short linear protein motif discovery

BackgroundShort 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.ResultsThe 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.ConclusionsProfile-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.

Prediction of Short Linear Protein Binding Regions

Short linear motifs in proteins (typically 3–12 residues in length) play key roles in protein–protein interactions by frequently binding specifically to peptide binding domains within interacting proteins. Their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here we present SLiMPred (short linear motif predictor), the first general de novo method designed to computationally predict such regions in protein primary sequences independent of experimentally defined homologs and interactors. The method applies machine learning techniques to predict new motifs based on annotated instances from the Eukaryotic Linear Motif database, as well as structural, biophysical, and biochemical features derived from the protein primary sequence. We have integrated these data sources and benchmarked the predictive accuracy of the method, and found that it performs equivalently to a predictor of protein binding regions in disordered regions, in addition to having predictive power for other classes of motif sites such as polyproline II helix motifs and short linear motifs lying in ordered regions. It will be useful in predicting peptides involved in potential protein associations and will aid in the functional characterization of proteins, especially of proteins lacking experimental information on structures and interactions. We conclude that, despite the diversity of motif sequences and structures, SLiMPred is a valuable tool for prioritizing potential interaction motifs in proteins.

Computational identification and analysis of protein short linear motifs

Short linear motifs (SLiMs) in proteins can act as targets for proteolytic cleavage, sites of post-translational modification, determinants of sub-cellular localization, and mediators of protein-protein interactions. Computational discovery of SLiMs involves assembling a group of proteins postulated to share a potential motif, masking out residues less likely to contain such a motif, down-weighting shared motifs arising through common evolutionary descent, and calculation of statistical probabilities allowing for the multiple testing of all possible motifs. Much of the challenge for motif discovery lies in the assembly and masking of datasets of proteins likely to share motifs, since the motifs are typically short (between 3 and 10 amino acids in length), so that potential signals can be easily swamped by the noise of stochastically recurring motifs. Focusing on disordered regions of proteins, where SLiMs are predominantly found, and masking out non-conserved residues can reduce the level of noise but more work is required to improve the quality of high-throughput experimental datasets (e.g. of physical protein interactions) as input for computational discovery.

Evidence for the Concerted Evolution between Short Linear Protein Motifs and Their Flanking Regions

BackgroundLinear 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.ResultsThe 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.ConclusionThe 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.

SLiMFinder: A Probabilistic Method for Identifying Over-Represented, Convergently Evolved, Short Linear Motifs in Proteins

BackgroundShort linear motifs (SLiMs) in proteins are functional microdomains of fundamental importance in many biological systems. SLiMs typically consist of a 3 to 10 amino acid stretch of the primary protein sequence, of which as few as two sites may be important for activity, making identification of novel SLiMs extremely difficult. In particular, it can be very difficult to distinguish a randomly recurring “motif” from a truly over-represented one. Incorporating ambiguous amino acid positions and/or variable-length wildcard spacers between defined residues further complicates the matter.Methodology/Principal FindingsIn this paper we present two algorithms. SLiMBuild identifies convergently evolved, short motifs in a dataset of proteins. Motifs are built by combining dimers into longer patterns, retaining only those motifs occurring in a sufficient number of unrelated proteins. Motifs with fixed amino acid positions are identified and then combined to incorporate amino acid ambiguity and variable-length wildcard spacers. The algorithm is computationally efficient compared to alternatives, particularly when datasets include homologous proteins, and provides great flexibility in the nature of motifs returned. The SLiMChance algorithm estimates the probability of returned motifs arising by chance, correcting for the size and composition of the dataset, and assigns a significance value to each motif. These algorithms are implemented in a software package, SLiMFinder. SLiMFinder default settings identify known SLiMs with 100% specificity, and have a low false discovery rate on random test data.Conclusions/SignificanceThe efficiency of SLiMBuild and low false discovery rate of SLiMChance make SLiMFinder highly suited to high throughput motif discovery and individual high quality analyses alike. Examples of such analyses on real biological data, and how SLiMFinder results can help direct future discoveries, are provided. SLiMFinder is freely available for download under a GNU license from http://bioinformatics.ucd.ie/shields/software/slimfinder/.