-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Structure 04/2013; · 6.35 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Nucleic Acids Research 11/2011; 40(Database issue):D935-9. · 8.03 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Current Opinion in Structural Biology 06/2011; 21(4):452-9. · 9.42 Impact Factor
-
David Gfeller,
Frank Butty,
Marta Wierzbicka,
Erik Verschueren, Peter Vanhee,
Haiming Huang,
Andreas Ernst,
Nisa Dar,
Igor Stagljar,
Luis Serrano,
Sachdev S Sidhu,
Gary D Bader,
Philip M Kim
[show abstract]
[hide abstract]
ABSTRACT: 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.
Molecular Systems Biology 04/2011; 7:484. · 8.63 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Trends in Biotechnology 02/2011; 29(5):231-9. · 9.15 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Nucleic Acids Research 10/2010; 39(Database issue):D435-42. · 8.03 Impact Factor
-
BMC Bioinformatics. 01/2010;
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Nucleic Acids Research 10/2009; 38(Database issue):D545-51. · 8.03 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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 A 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 A 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.
Structure 09/2009; 17(8):1128-36. · 6.35 Impact Factor
