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Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders. ESCRT-III is involved in the endolysosomal system and disturbed in neurodegenerative diseases. Here the authors show that disruption of an interaction between ESCRT-III member CHMP2B and α-synuclein by a peptide inhibitor mitigates neurodegeneration in Parkinson's disease models. [ABSTRACT FROM AUTHOR]

Summary Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions. Availability and implementation The model implementation is available at https://gitlab.com/mcfeemat/gdockscore. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM‐ligand complexes, indicating that a large majority of the phage‐derived peptides are likely to target natural peptide‐binding sites and could thus act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm‐db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs. SYNOPSIS: A database of thousands of peptides targeting hundreds of diverse peptide recognition modules is presented (www.prm‐db.org). Applications of this large dataset include exploring natural protein‐protein interactions and inhibitor design. The database contains 7,984 unique peptides, each binding to one of 500 peptide recognition modules (PRMs) representing 82 distinct structural families.Phage‐derived peptides reflect the hydrophobic character of natural peptides at specific positions critical for PRM recognition.Comparative structural analysis highlights the functional importance of specific positions in peptide binding profiles.Phage‐derived peptides resemble natural short linear motifs (SLiMs) and likely bind to the same sites. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The intrinsically disordered regions of eukaryotic proteomes are enriched in short linear motifs ( SLiMs), which are of crucial relevance for cellular signaling and protein regulation; many mediate interactions by providing binding sites for peptide-binding domains. The vast majority of SLiMs remain to be discovered highlighting the need for experimental methods for their large-scale identification. We present a novel proteomic peptide phage display (ProP- PD) library that displays peptides representing the disordered regions of the human proteome, allowing direct large-scale interrogation of most potential binding SLiMs in the proteome. The performance of the ProP- PD library was validated through selections against SLiM-binding bait domains with distinct folds and binding preferences. The vast majority of identified binding peptides contained sequences that matched the known SLiM-binding specificities of the bait proteins. For SHANK1 PDZ, we establish a novel consensus TxF motif for its non-C-terminal ligands. The binding peptides mostly represented novel target proteins, however, several previously validated protein-protein interactions (PPIs) were also discovered. We determined the affinities between the VHS domain of GGA1 and three identified ligands to 40-130 μ m through isothermal titration calorimetry, and confirmed interactions through coimmunoprecipitation using full-length proteins. Taken together, we outline a general pipeline for the design and construction of ProP- PD libraries and the analysis of ProP- PD-derived, SLiM-based PPIs. We demonstrated the methods potential to identify low affinity motif-mediated interactions for modular domains with distinct binding preferences. The approach is a highly useful complement to the current toolbox of methods for PPI discovery. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery. [ABSTRACT FROM AUTHOR]

Syntenin has crucial roles in cell adhesion, cell migration and synaptic transmission. Its closely linked postsynaptic density-95, discs large 1, zonula occludens-1 (PDZ) domains typically interact with C-terminal ligands. We profile syntenin PDZ1-2 through proteomic peptide phage display (ProP-PD) using a library that displays C-terminal regions of the human proteome. The protein recognizes a broad range of peptides, with a preference for hydrophobic motifs and has a tendency to recognize cryptic internal ligands. We validate the interaction with nectin-1 through orthogonal assays. The study demonstrates the power of ProP-PD as a complementary approach to uncover interactions of potential biological relevance. [ABSTRACT FROM AUTHOR]

Intrinsically disordered regions have been associated with various cellular processes and are implicated in several human diseases, but their exact roles remain unclear. We previously defined two classes of conserved disordered regions in budding yeast, referred to as ''flexible'' and ''constrained'' conserved disorder. In flexible disorder, the property of disorder has been positionally conserved during evolution, whereas in constrained disorder, both the amino acid sequence and the property of disorder have been conserved. Here, we show that flexible and constrained disorder are widespread in the human proteome, and are particularly common in proteins with regulatory functions. Both classes of disordered sequences are highly enriched in regions of proteins that undergo tissue-specific (TS) alternative splicing (AS), but not in regions of proteins that undergo general (i.e., not tissue-regulated) AS. Flexible disorder is more highly enriched in TS alternative exons, whereas constrained disorder is more highly enriched in exons that flank TS alternative exons. These latter regions are also significantly more enriched in potential phosphosites and other short linear motifs associated with cell signaling. We further show that cancer driver mutations are significantly enriched in regions of proteins associated with TS and general AS. Collectively, our results point to distinct roles for TS alternative exons and flanking exons in the dynamic regulation of protein interaction networks in response to signaling activity, and they further suggest that alternatively spliced regions of proteins are often functionally altered by mutations responsible for cancer. [ABSTRACT FROM AUTHOR]

Intracellular juxtamembrane regions of transmembrane proteins play pivotal roles in cell signalling, mediated by protein-protein interactions. Disordered protein regions, and short conserved motifs within them, are emerging as key determinants of many such interactions. Here, we investigated whether disorder and conserved motifs are enriched in the juxtamembrane area of human single-pass transmembrane proteins. Conserved motifs were defined as short disordered regions that were much more conserved than the adjacent disordered residues. Human single-pass proteins had higher mean disorder in their cytoplasmic segments than their extracellular parts. Some, but not all, of this effect reflected the shorter length of the cytoplasmic tail. A peak of cytoplasmic disorder was seen at around 30 residues from the membrane. We noted a significant increase in the incidence of conserved motifs within the disordered regions at the same location, even after correcting for the extent of disorder. We conclude that elevated disorder within the cytoplasmic tail of many transmembrane proteins is likely to be associated with enrichment for signalling interactions mediated by conserved short motifs. [ABSTRACT FROM AUTHOR]

Abstract: Peptide-binding domains play a critical role in regulation of cellular processes by mediating protein interactions involved in signalling. In recent years, the development of large-scale technologies has enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. These efforts have provided significant insights into the binding specificities of these modular domains. Many research groups have taken advantage of this unprecedented volume of specificity data and have developed a variety of new algorithms for the prediction of binding specificities of peptide-binding domains and for the prediction of their natural binding targets. This knowledge has also been applied to the design of synthetic peptide-binding domains in order to rewire protein–protein interaction networks. Here, we describe how these experimental technologies have impacted on our understanding of peptide-binding domain specificities and on the elucidation of their natural ligands. We discuss SH3 and PDZ domains as well characterized examples, and we explore the feasibility of expanding high-throughput experiments to other peptide-binding domains. [Copyright &y& Elsevier]

The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules. [ABSTRACT FROM AUTHOR]

Background: Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy. Results: We developed an efficient search algorithm to scan the target proteome for potential domain targets and to increase the accuracy of each hit by integrating a variety of pre-computed features, such as conservation, surface propensity, and disorder. The integration is performed using naïve Bayes and a training set of validated experiments. Conclusions: By integrating a variety of biologically relevant features to predict domain targets, we demonstrated a notably improved prediction of modular protein domain targets. Combined with emerging high-resolution data of domain specificities, we believe that our approach can assist in the reconstruction of many signaling pathways. [ABSTRACT FROM AUTHOR]

ELASPIC is a novel ensemble machine-learning approach that predicts the effects of mutations on protein folding and protein--protein interactions. Here, we present the ELASPIC webserver, which makes the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in the Uniprot database, and allows all predicted results, including modeled wild-type and mutated structures, to be managed and viewed online and downloaded if needed. It is backed by a database which contains improved structural domain definitions, and a list of curated domain--domain interactions for all known proteins, as well as homology models of domains and domain--domain interactions for the human proteome. Homology models for proteins of other organisms are calculated on the fly, and mutations are evaluated within minutes once the homology model is available. [ABSTRACT FROM AUTHOR]

It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins ("hubs"). As a complementary notion, it is possible to define bottlenecks as proteins with a high betweenness centrality (i.e., network nodes that have many ''shortest paths'' going through them, analogous to major bridges and tunnels on a highway map). Bottlenecks are, in fact, key connector proteins with surprising functional and dynamic properties. In particular, they are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., ''bottleneck-ness'') is a much more significant indicator of essentiality than degree (i.e., ''hub-ness''). Furthermore, bottlenecks correspond to the dynamic components of the interaction network-they are significantly less well coexpressed with their neighbors than nonbottlenecks, implying that expression dynamics is wired into the network topology. [ABSTRACT FROM AUTHOR]

Most studies of protein networks operate on a high level of abstraction, neglecting structural and chemical aspects of each interaction. Here, we characterize interactions by using atomic-resolution information from three-dimensional protein structures. We find that some previously recognized relationships between network topology and genomic features (e.g., hubs tending to be essential proteins) are actually more reflective of a structural quantity, the number of distinct binding interfaces. Subdividing hubs with respect to this quantity provides insight into their evolutionary rate and indicates that additional mechanisms of network growth are active in evolution (beyond effective preferential attachment through gene duplication). [ABSTRACT FROM AUTHOR]

Background: The metalloprotease-disintegrin family, or ADAM, proteins, are implicated in cell-cell interactions, cell fusion, and cell signaling, and are widely distributed among metazoan phyla. Orthologous relationships have been defined for a few ADAM proteins including ADAM10 (Kuzbanian), and ADAM17 (TACE), but evolutionary relationships are not clear for the majority of family members. Human ADAM33 refers to a testis cDNA clone that does not contain a complete open reading frame, but portions of the predicted protein are similar to Xenopus laevis ADAM13. Results: In a 48 kb region of mouse DNA adjacent to the Attractin gene on mouse chromosome 2, we identified sequences very similar to human ADAM33. A full-length mouse cDNA was identified by a combination of gene prediction programs and RT-PCR, and the probable full-length human cDNA was identified by comparison to human genomic sequence in the homologous region on chromosome 20p13. Mouse ADAM33 is 44% identical to Xenopus laevis ADAM13, however a phylogenetic alignment and consideration of functional domains suggests that the two genes are not orthologous. Mouse Adam33 is widely expressed, most highly in the adult brain, heart, kidney, lung and testis. Conclusions: While mouse ADAM33 is similar to Xenopus ADAM13 in sequence, further examination of its embryonic expression pattern, catalytic activity and protein interactions will be required to assess the functional relationship between these two proteins. Adam33 is expressed in the mouse adult brain and could play a role in complex processes that require cell-cell communication. [ABSTRACT FROM AUTHOR]

The article offers information on the detection of protein network with three-dimensional structure in human body. It mentions that fully programmed pipeline that models three-dimensional (3D) structural details of protein interactions have capacity to allow researchers to interrogate perturbation effects within protein pathways. It further highlights regarding homology modeling for prediction of three-dimensional models for a given protein sequence.

The article presents an overview of the various classifications of intrinsically disordered proteins (iDPs) and intrinsically disordered regions (iDRs). It discusses diverse classification approaches based on function, like chaperone protein, functional features, structure, sequence, protein interactions, evolution, regulation, and biophysical properties of these proteins. Also discussed are Linear Motif-Based approaches for function prediction for IDPs and IDPRs.

WW domains are small modules present in regulatory and signaling proteins that mediate specific protein-protein interactions. The WW domain-containing oxidoreductase (WWOX) encodes a 46-kDa tumor suppressor that contains two N-terminal WW domains and a central short-chain dehydrogenase/reductase domain. Based on its ligand recognition motifs, the WW domain family is classified into four groups. The largest one, to which WWOX belongs, recognizes ligands with a PPXY motif. To pursue the functional properties of the WW domains of WWOX, we employed mass spectrometry and phage display experiments to identify putative WWOX-interacting partners. Our analysis revealed that the first WW (WW1) domain of WWOX is the main functional interacting domain. Furthermore, our study uncovered well known and new PPXY-WW1-interacting partners and shed light on novel LPXY-WW1-interacting partners of WWOX. Many of these proteins are components of multiprotein complexes involved in molecular processes, including transcription, RNA processing, tight junction, and metabolism. By utilizing GST pull-down and immunoprecipitation assays, we validated that WWOX is a substrate of the E3 ubiquitin ligase ITCH, which contains two LPXY motifs. We found that ITCH mediates Lys-63-linked polyubiquitination of WWOX, leading to its nuclear localization and increased cell death. Our data suggest that the WW1 domain of WWOX provides a versatile platform that links WWOX with individual proteins associated with physiologically important networks. [ABSTRACT FROM AUTHOR]

Abstract: Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems’ perspective we need to construct complete and accurate host–pathogen protein–protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host–pathogen interactions. As an application example of the methods covered, we predict host–pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research. [Copyright &y& Elsevier]

Summary: Alternative splicing plays a key role in the expansion of proteomic and regulatory complexity, yet the functions of the vast majority of differentially spliced exons are not known. In this study, we observe that brain and other tissue-regulated exons are significantly enriched in flexible regions of proteins that likely form conserved interaction surfaces. These proteins participate in significantly more interactions in protein-protein interaction (PPI) networks than other proteins. Using LUMIER, an automated PPI assay, we observe that approximately one-third of analyzed neural-regulated exons affect PPIs. Inclusion of these exons stimulated and repressed different partner interactions at comparable frequencies. This assay further revealed functions of individual exons, including a role for a neural-specific exon in promoting an interaction between Bridging Integrator 1 (Bin1)/Amphiphysin II and Dynamin 2 (Dnm2) that facilitates endocytosis. Collectively, our results provide evidence that regulated alternative exons frequently remodel interactions to establish tissue-dependent PPI networks. [ABSTRACT FROM AUTHOR]

Summary SH3 domains are protein modules that mediate protein-protein interactions in many eukaryotic signal transduction pathways. The majority of SH3 domains studied thus far act by binding to proline-rich sequences in partner proteins, but a growing number of studies have revealed alternative recognition mechanisms. We have comprehensively surveyed the specificity landscape of human SH3 domains in an unbiased manner using peptide-phage display and deep sequencing. Based on ∼70,000 unique binding peptides, we obtained 154 specificity profiles for 115 SH3 domains, which reveal that roughly half of the SH3 domains exhibit non-canonical specificities and collectively recognize a wide variety of peptide motifs, most of which were previously unknown. Crystal structures of SH3 domains with two distinct non-canonical specificities revealed novel peptide-binding modes through an extended surface outside of the canonical proline-binding site. Our results constitute a significant contribution toward a complete understanding of the mechanisms underlying SH3-mediated cellular responses. [ABSTRACT FROM AUTHOR]