We have identified a novel gene in a genome-wide, double-strand break DNA repair RNAi screen and show that is involved in the neurological disease hereditary spastic paraplegia.

The human proteome contains a plethora of short linear motifs (SLiMs) that serve as binding interfaces for modular protein domains. Such interactions are crucial for signaling and other cellular processes, but are difficult to detect because of their low to moderate affinities. Here we developed a dedicated approach, proteomic peptide-phage display (ProP-PD), to identify domain-SLiM interactions. Specifically, we generated phage libraries containing all human and viral C-terminal peptides using custom oligonucleotide microarrays. With these libraries we screened the nine PSD-95/ Dlg/ZO-1 (PDZ) domains of human Densin-180, Erbin, Scribble, and Disks large homolog 1 for peptide ligands. We identified several known and putative interactions potentially relevant to cellular signaling pathways and confirmed interactions between fulllength Scribble and the target proteins β-PIX, plakophilin-4, and guanylate cyclase soluble subunit α-2 using colocalization and coimmunoprecipitation experiments. The affinities of recombinant Scribble PDZ domains and the synthetic peptides representing the C termini of these proteins were in the 1-to 40-μM range. Furthermore, we identified several well-established host-virus proteinprotein interactions, and confirmed that PDZ domains of Scribble interact with the C terminus of Tax-1 of human T-cell leukemia virus with micromolar affinity. Previously unknown putative viral protein ligands for the PDZ domains of Scribble and Erbin were also identified. Thus, we demonstrate that our ProP-PD libraries are useful tools for probing PDZ domain interactions. The method can be extended to interrogate all potential eukaryotic, bacterial, and viral SLiMs and we suggest it will be a highly valuable approach for studying cellular and pathogen-host protein-protein interactions.T here are an estimated 650,000 protein-protein interactions in a human cell (1). These interactions are integral to cellular function and mediate signaling pathways that are often misregulated in cancer (2) and may be hijacked by viral proteins (3). Commonly, signaling pathways involve moderate affinity interactions between modular domains and short linear motifs (SLiMs; conserved 2-to 10-aa stretches in disordered regions) (4) that are difficult to capture using high-throughput methods, such as yeast two-hybrid (Y2H) or affinity-purification mass spectrometry (AP/MS) but can be identified using peptide arrays, splitprotein systems (5, 6), or peptide-phage display (7-10). A major limitation of peptide arrays is coverage, because the number of potential binding peptides in the proteome is orders of magnitude larger than what can be printed on an array. Conventional phage libraries display combinatorially generated peptide sequences that can identify biophysically optimal ligands of modular domains but this approach can exhibit a hydrophobic bias and may not be ideal for detecting natural binders (11). Thus, there is a need for alternative approaches for identification of relevant domain-SLiMs interactions.Here, we report an appr...

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.

Highlights d Introduces a new experimental method for protein structure determination called 3Dseq d Experimental evolution generates hundreds of thousands of functional sequences d Analysis of sequence co-variation patterns yields accurate residue interactions d Molecular dynamics with interaction constraints produces correct protein folds

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

No abstract

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein–protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-011-9433-1) contains supplementary material, which is available to authorized users.

Background: Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design.