Minimotif Miner 4: a million peptide minimotifs and counting

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Interaction modules that impart specificity to disordered protein

Intrinsically disordered regions (IDRs) are especially enriched among proteins that regulate chromatin and transcription. As a result, mechanisms that influence specificity of IDR-driven interactions have emerged as exciting unresolved issues for understanding gene regulation. We review the molecular elements frequently found within IDRs that confer regulatory specificity. In particular, we summarize the differing roles of disordered low-complexity regions (LCRs) and short linear motifs (SLiMs) towards selective nuclear regulation. Examination of IDR-driven interactions highlights SLiMs as organizers of selectivity, with widespread roles in gene regulation and integration of cellular signals. Analysis of recurrent interactions between SLiMs and folded domains suggests diverse avenues for SLiMs to influence phase-separated condensates and highlights opportunities to manipulate these interactions for control of biological activity.

Systematic Assessment of Protein C-Termini Mutated in Human Disorders

All proteins have a carboxyl terminus, and we previously summarized eight mutations in binding and trafficking sequence determinants in the C-terminus that, when disrupted, cause human diseases. These sequence elements for binding and trafficking sites, as well as post-translational modifications (PTMs), are called minimotifs or short linear motifs. We wanted to determine how frequently mutations in minimotifs in the C-terminus cause disease. We searched specifically for PTMs because mutation of a modified amino acid almost always changes the chemistry of the side chain and can be interpreted as loss-of-function. We analyzed data from ClinVar for disease variants, Minimotif Miner and the C-terminome for PTMs, and RefSeq for protein sequences, yielding 20 such potential disease-causing variants. After additional screening, they include six with a previously reported PTM disruption mechanism and nine with new hypotheses for mutated minimotifs in C-termini that may cause disease. These mutations were generally for different genes, with four different PTM types and several different diseases. Our study helps to identify new molecular mechanisms for nine separate variants that cause disease, and this type of analysis could be extended as databases grow and to binding and trafficking motifs. We conclude that mutated motifs in C-termini are an infrequent cause of disease.

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.

The HGR motif is the antiangiogenic determinant of vasoinhibin: implications for a therapeutic orally active oligopeptide

The hormone prolactin acquires antiangiogenic and antivasopermeability properties after undergoing proteolytic cleavage to vasoinhibin, an endogenous prolactin fragment of 123 or more amino acids that inhibits the action of multiple proangiogenic factors. Preclinical and clinical evidence supports the therapeutic potential of vasoinhibin against angiogenesis-related diseases including diabetic retinopathy, peripartum cardiomyopathy, rheumatoid arthritis, and cancer. However, the use of vasoinhibin in the clinic has been limited by difficulties in its production. Here, we removed this barrier to using vasoinhibin as a therapeutic agent by showing that a short linear motif of just three residues (His46-Gly47-Arg48) (HGR) is the functional determinant of vasoinhibin. The HGR motif is conserved throughout evolution, its mutation led to vasoinhibin loss of function, and oligopeptides containing this sequence inhibited angiogenesis and vasopermeability with the same potency as whole vasoinhibin. Furthermore, the oral administration of an optimized cyclic retro-inverse vasoinhibin heptapeptide containing HGR inhibited melanoma tumor growth and vascularization in mice and exhibited equal or higher antiangiogenic potency than other antiangiogenic molecules currently used as anti-cancer drugs in the clinic. Finally, by unveiling the mechanism that obscures the HGR motif in prolactin, we anticipate the development of vasoinhibin-specific antibodies to solve the on-going challenge of measuring endogenous vasoinhibin levels for diagnostic and interventional purposes, the design of vasoinhibin antagonists for managing insufficient angiogenesis, and the identification of putative therapeutic proteins containing HGR.

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

On the evolution of protein-adenine binding

Proteins' interactions with ancient ligands may reveal how molecular recognition emerged and evolved. We explore how proteins recognize adenine: a planar rigid fragment found in the most common and ancient ligands. We have developed a computational pipeline that extracts protein-adenine complexes from the Protein Data Bank, structurally superimposes their adenine fragments, and detects the hydrogen bonds mediating the interaction. Our analysis extends the known motifs of protein-adenine interactions in the Watson-Crick edge of adenine and shows that all of adenine's edges may contribute to molecular recognition. We further show that, on the proteins' side, binding is often mediated by specific amino acid segments ("themes") that recur across different proteins, such that different proteins use the same themes when binding the same adenine-containing ligands. We identify numerous proteins that feature these themes and are thus likely to bind adenine-containing ligands. Our analysis suggests that adenine binding has emerged multiple times in evolution.

The carboxy-terminus, a key regulator of protein function

All proteins end with a carboxyl terminus that has unique biophysical properties and is often disordered. Although there are examples of important C-termini functions, a more global role for the C-terminus is not yet established. In this review, we summarize research on C-termini, a unique region in proteins that cells exploit. Alternative splicing and proteolysis increase the diversity of proteins and peptides in cells with unique C-termini. The C-termini of proteins contain minimotifs, short peptides with an encoded function generally characterized as binding, posttranslational modifications, and trafficking. Many of these activities are specific to minimotifs on the C-terminus. Approximately 13% of C-termini in the human proteome have a known minimotif, and the majority, if not all of the remaining termini have conserved motifs inferring a function that remains to be discovered. C-termini, their predictions, and their functions are collated in the C-terminome, Proteus, and Terminus Oriented Protein Function INferred Database (TopFIND) database/web systems. Many C-termini are well conserved, and some have a known role in health and disease. We envision that this summary of C-termini will guide future investigation of their biochemical and physiological significance.

SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

Minimotifs dysfunction is pervasive in neurodegenerative disorders

Minimotifs are modular contiguous peptide sequences in proteins that are important for posttranslational modifications, binding to other molecules, and trafficking to specific subcellular compartments. Some molecular functions of proteins in cellular pathways can be predicted from minimotif consensus sequences identified through experimentation. While a role for minimotifs in regulating signal transduction and gene regulation during disease pathogenesis (such as infectious diseases and cancer) is established, the therapeutic use of minimotif mimetic drugs is limited. In this review, we discuss a general theme identifying a pervasive role of minimotifs in the pathomechanism of neurodegenerative diseases. Beyond their longstanding history in the genetics of familial neurodegeneration, minimotifs are also major players in neurotoxic protein aggregation, aberrant protein trafficking, and epigenetic regulation. Generalizing the importance of minimotifs in neurodegenerative diseases offers a new perspective for the future study of neurodegenerative mechanisms and the investigation of new therapeutics.

PSSMSearch: a server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants

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/.

The present and the future of motif-mediated protein-protein interactions

Protein-protein interactions (PPIs) are essential to governing virtually all cellular processes. Of particular importance are the versatile motif-mediated interactions (MMIs), which are thus far underrepresented in available interaction data. This is largely due to technical difficulties inherent in the properties of MMIs, but due to the increasing recognition of the vital roles of MMIs in biology, several systematic approaches have recently been developed to detect novel MMIs. Consequently, rapidly growing numbers of motifs are being identified and pursued further for therapeutic applications. In this review, we discuss the current understanding on the diverse functions and disease-relevance of MMIs, the key methodologies for detection of MMIs, and the potential of MMIs for drug development.