The number of protein-peptide interactions in a cell is so large that experimental determination of all these complex structures would be a daunting task. Although homology modeling and refinement protocols have vastly improved the number and quality of predicted structural models, ab initio methods are still challenged by both the large number of possible docking sites and the conformational space accessible to flexible peptides. We present a method that addresses these challenges by sampling the entire accessible surface of a protein with a reduced conformational space of interacting backbone fragment pairs from unrelated structures. We demonstrate its potential by predicting ab initio the bound structure for a variety of protein-peptide complexes. In addition, we show the potential of our method for the discovery of domain interaction sites and domain-domain docking.

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.

SUMMARY:Protein interaction networks are widely used to depict the relationships between proteins. These networks often lack the information on physical binary interactions, and they do not inform whether there is incompatibility of structure between binding partners. Here, we introduce SAPIN, a framework dedicated to the structural analysis of protein interaction networks. SAPIN first identifies the protein parts that could be involved in the interaction and provides template structures. Next, SAPIN performs structural superimpositions to identify compatible and mutually exclusive interactions. Finally, the results are displayed using Cytoscape Web.AVAILABILITY:The SAPIN server is available at http://sapin.crg.es.CONTACT:jae-seong.yang@crg.eu or christina.kiel@crg.eu.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics Online.

Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks. Modular protein domains have a central role in the complex network of signaling pathways that governs cellular processes. Many of them, called peptide recognition domains, bind short linear regions in their target proteins, such as the well‐known SH3 or PDZ domains. These domain–peptide interactions are the predominant form of protein interaction in signaling pathways. Because of the relative simplicity of the interaction, their binding specificity is generally represented using a simple model, analogous to transcription factor binding: the domain binds a short stretch of amino acids and at each position some amino acids are preferred over other ones. Thus, for each position, a probability can be assigned to each amino acid and these probabilities are often grouped into a matrix called position weight matrix (PWM) or position‐specific scoring matrix. Such a matrix can then be represented in a highly intuitive manner as a so‐called sequence logo (see Figure 1). A main shortcoming of this specificity model is that, although intuitive and interpretable, it inherently assumes that all residues in the peptide contribute independently to binding. On the basis of statistical analyses of large data sets of peptides binding to PDZ, SH3 and WW domains, we show that for most domains, this is not the case. Indeed, there is complex and highly significant interplay between the ligand residues. To overcome this issue, we develop a computational model that can both take into account such correlations and also preserve the advantages of PWMs, namely its straightforward interpretability. Briefly, our method detects whether the domain is capable of binding its targets not only with a single specificity but also with multiple specificities. If so, it will determine all the relevant specificities (see Figure 1). This is accomplished by using a machine learning algorithm based on mixture models, and the results can be effectively visualized as multiple sequence logos. In other words, based on experimentally derived data sets of binding peptides, we determine for every domain, in addition to the known specificity, one or more new specificities. As such, we capture more real information, and our model performs better than previous models of binding specificity. A crucial question is what these new specificities correspond to: are they simply mathematical artifacts coming out of some algorithm or do they represent something we can understand on a biophysical or structural level? Overall, the new specificities provide us with substantial new intuitive insight about the structural basis of binding for these domains. We can roughly identify two cases. First, we have neighboring (or very close in sequence) amino acids in the ligand that show significant correlations. These usually correspond to amino acids whose side chains point in the same directions and often occupy the same physical space, and therefore can directly influence each other. In other cases, we observe that multiple specificities found for a single domain are very different from each other. They correspond to different ways that the domain accommodates its binders. Often, conformational changes are required to switch from one binding mode to another. In almost all cases, only one canonical binding mode was previously known, and our analysis enables us to predict several interesting non‐canonical ones. Specifically, we discuss one example in detail in Figure 5. In a PDZ domain of DLG1, we identify a novel binding specificity that differs from the canonical one by the presence of an additional tryptophan at the C terminus of the ligand. From a structural point of view, this would require a flexible loop to move out of the way to accommodate this rather large side chain. We find evidence of this predicted new binding mode based on both existing crystal structures and structural modeling. Finally, our model of binding specificity leads to predictions of many new and previously unknown protein interactions. We validate a number of these using the membrane yeast two‐hybrid approach. In summary, we show here that multiple specificity is a general and underappreciated phenomenon for modular peptide recognition domains and that it leads to substantial new insight into the basis of protein interactions.

Peptides possess several attractive features when compared to small molecule and protein therapeutics, such as high structural compatibility with target proteins, the ability to disrupt protein protein interfaces, and small size. Efficient design of high-affinity peptide ligands via rational methods has been a major obstacle to the development of this potential drug class. However, structural insights into the architecture of protein peptide interfaces have recently culminated in several computational approaches for the rational design of peptides that target proteins. These methods provide a valuable alternative to experimental high-resolution structures of target protein-peptide complexes, bringing closer the dream of in silico designed peptides for therapeutic applications.

Structure-based computational methods are popular tools for designing proteins and interactions between proteins because they provide the necessary insight and details required for rational engineering. Here, we first argue that large-scale databases of fragments contain a discrete but complete set of building blocks that can be used to design structures. We show that these structural alphabets can be saturated to provide conformational ensembles that sample the native structure space around energetic minima. Second, we show that catalogs of interaction patterns hold the key to overcome the lack of scaffolds when computationally designing protein interactions. Finally, we illustrate the power of database-driven computational protein design methods by recent successful applications and discuss what challenges remain to push this field forward.

High-resolution structures of proteins remain the most valuable source for understanding their function in the cell and provide leads for drug design. Since the availability of sufficient protein structures to tackle complex problems such as modeling backbone moves or docking remains a problem, alternative approaches using small, recurrent protein fragments have been employed. Here we present two databases that provide a vast resource for implementing such fragment-based strategies. The BriX database contains fragments from over 7000 non-homologous proteins from the Astral collection, segmented in lengths from 4 to 14 residues and clustered according to structural similarity, summing up to a content of 2 million fragments per length. To overcome the lack of loops classified in BriX, we constructed the Loop BriX database of non-regular structure elements, clustered according to end-to-end distance between the regular residues flanking the loop. Both databases are available online (http://brix.crg.es) and can be accessed through a user-friendly web-interface. For high-throughput queries a web-based API is provided, as well as full database downloads. In addition, two exciting applications are provided as online services: (i) user-submitted structures can be covered on the fly with BriX classes, representing putative structural variation throughout the protein and (ii) gaps or low-confidence regions in these structures can be bridged with matching fragments.

An estimated 15-40% of all interactions in the cell are mediated through protein-peptide interactions [1,2] meaning that, at the most extreme, nearly every protein is affected either directly or indirectly by peptide-binding events.

Although protein-peptide interactions are estimated to constitute up to 40% of all protein interactions, relatively little information is available for the structural details of these interactions. Peptide-mediated interactions are a prime target for drug design because they are predominantly present in signaling and regulatory networks. A reliable data set of nonredundant protein-peptide complexes is indispensable as a basis for modeling and design, but current data sets for protein-peptide interactions are often biased towards specific types of interactions or are limited to interactions with small ligands. In PepX (http://pepx.switchlab.org), we have designed an unbiased and exhaustive data set of all protein-peptide complexes available in the Protein Data Bank with peptide lengths up to 35 residues. In addition, these complexes have been clustered based on their binding interfaces rather than sequence homology, providing a set of structurally diverse protein-peptide interactions. The final data set contains 505 unique protein-peptide interface clusters from 1431 complexes. Thorough annotation of each complex with both biological and structural information facilitates searching for and browsing through individual complexes and clusters. Moreover, we provide an additional source of data for peptide design by annotating peptides with naturally occurring backbone variations using fragment clusters from the BriX database.

This paper describes the development of a high quality information platform by the use of Web 2.0 technologies to support and enhance student mobility in higher education. It discusses the success of social networks and the role of universities. It describes the current developed projects by the Erasmus Student Network and how they can be considered as the key-elements in the creation of this platform. The paper ends with an example-project applied to the 20th anniversary of the Erasmus Program.

We compared the modes of interaction between protein-peptide interfaces and those observed within monomeric proteins and found surprisingly few differences. Over 65% of 731 protein-peptide interfaces could be reconstructed within 1 Å RMSD using solely fragment interactions occurring in monomeric proteins. Interestingly, more than 80% of interacting fragments used in reconstructing a protein-peptide binding site were obtained from monomeric proteins of an entirely different structural classification, with an average sequence identity below 15%. Nevertheless, geometric properties perfectly match the interaction patterns observed within monomeric proteins. We show the usefulness of our approach by redesigning the interaction scaffold of nine protein-peptide complexes, for which five of the peptides can be modeled within 1 Å RMSD of the original peptide position. These data suggest that the wealth of structural data on monomeric proteins could be harvested to model protein-peptide interactions and, more importantly, that sequence homology is no prerequisite.