Protein-Peptide Interactions Adopt the Same Structural Motifs as Monomeric Protein Folds

In silico and in vitro elucidation of BH3 binding specificity toward Bcl-2

Interactions between Bcl-2-like proteins and BH3 domains play a key role in the regulation of apoptosis. Despite the overall structural similarity of their interaction with helical BH3 domains, Bcl-2-like proteins exhibit an intricate spectrum of binding specificities whose underlying basis is not well understood. Here, we characterize these interactions using Rosetta FlexPepBind, a protocol for the prediction of peptide binding specificity that evaluates the binding potential of different peptides based on structural models of the corresponding peptide-receptor complexes. For two prominent players, Bcl-xL and Mcl-1, we obtain good agreement with a large set of experimental SPOT array measurements and recapitulate the binding specificity of peptides derived by yeast display in a previous study. We extend our approach to a third member of this family, Bcl-2: we test our blind prediction of the binding of 180 BIM-derived peptides with a corresponding experimental SPOT array. Both prediction and experiment reveal a Bcl-2 binding specificity pattern that resembles that of Bcl-xL. Finally, we extend this application to accurately predict the specificity pattern of additional human BH3-only derived peptides. This study characterizes the distinct patterns of binding specificity of BH3-only derived peptides for the Bcl-2 like proteins Bcl-xL, Mcl-1, and Bcl-2 and provides insight into the structural basis of determinants of specificity.

Coupled folding-binding in a hydrophobic/polar protein model: impact of synergistic folding and disordered flanks

Coupled folding-binding is central to the function of many intrinsically disordered proteins, yet not fully understood. With a continuous three-letter protein model, we explore the free-energy landscape of pairs of interacting sequences and how it is impacted by 1), variations in the binding mechanism; and 2), the addition of disordered flanks to the binding region. In particular, we focus on two sequences, one with 16 and one with 35 amino acids, which make a stable dimeric three-helix bundle at low temperatures. Three distinct binding mechanisms are realized by altering the stabilities of the individual monomers: docking, coupled folding-binding of a single α-helix, and synergistic folding and binding. Compared to docking, the free-energy barrier for binding is reduced when the single α-helix is allowed to fold upon binding, but only marginally. A greater reduction is found for synergistic folding, which in addition results in a binding transition state characterized by very few interchain contacts. Disordered flanking chain segments attached to the α-helix sequence can, despite a negligible impact on the dimer stability, lead to a downhill free-energy surface in which the barrier for binding is eliminated.

The construction of "phylomer" peptide libraries as a rich source of potent inhibitors of protein/protein interactions

Phylomer libraries are made from random overlapping genome fragments of biodiverse bacteria and Archaea. They provide a rich source of high-affinity binders to protein interfaces, and can be used both for target-directed screening approaches and for phenotypic screens to discover new targets. Here, we describe methods used for the construction of a Phylomer library, illustrated by examples of construction in both a yeast two-hybrid vector and a phage display vector.

Order and disorder in large multi-site docking proteins of the Gab family--implications for signalling complex formation and inhibitor design strategies

Large multi-site docking (LMD) proteins of the Gab, IRS, FRS, DOK and Cas families consist of one or two folded N-terminal domains, followed by a predominantly disordered C-terminal extension. Their primary function is to provide a docking platform for signalling molecules (including PI3K, PLC, Grb2, Crk, RasGAP, SHP2) in intracellular signal transmission from activated cell-surface receptors, to which they become coupled. A detailed analysis of the structural nature and intrinsic disorder propensity of LMD proteins, with Gab proteins as specific examples, is presented. By primary sequence analysis and literature review the varying levels of disorder and hidden order are predicted, revealing properties and a physical architecture that help to explain their biological function and characteristics, common for network hub proteins. The virulence factor, CagA, from Helicobacter pylori is able to mimic Gab function once injected by this human pathogen into stomach epithelial cells. Its predicted differential structure is compared to Gab1 with respect to its functional mimicry. Lastly, we discuss how LMD proteins, in particular Gab1 and Gab2, and their protein partners, such as SH2 and SH3 domain-containing adaptors like Grb2, might qualify for future anti-cancer strategies in developing protein-protein interaction (PPI) inhibitors towards binary interactors consisting of an intrinsically disordered epitope and a structured domain surface.

Protein design with fragment databases

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.

CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs

CSpritz is a web server for the prediction of intrinsic protein disorder. It is a combination of previous Spritz with two novel orthogonal systems developed by our group (Punch and ESpritz). Punch is based on sequence and structural templates trained with support vector machines. ESpritz is an efficient single sequence method based on bidirectional recursive neural networks. Spritz was extended to filter predictions based on structural homologues. After extensive testing, predictions are combined by averaging their probabilities. The CSpritz website can elaborate single or multiple predictions for either short or long disorder. The server provides a global output page, for download and simultaneous statistics of all predictions. Links are provided to each individual protein where the amino acid sequence and disorder prediction are displayed along with statistics for the individual protein. As a novel feature, CSpritz provides information about structural homologues as well as secondary structure and short functional linear motifs in each disordered segment. Benchmarking was performed on the very recent CASP9 data, where CSpritz would have ranked consistently well with a Sw measure of 49.27 and AUC of 0.828. The server, together with help and methods pages including examples, are freely available at URL: http://protein.bio.unipd.it/cspritz/.

Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.

Computational design of peptide ligands

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.

Modeling peptide-protein interactions

Peptide-protein interactions are prevalent in the living cell and form a key component of the overall protein-protein interaction network. These interactions are drawing increasing interest due to their part in signaling and regulation, and are thus attractive targets for computational structural modeling. Here we report an overview of current techniques for the high resolution modeling of peptide-protein complexes. We dissect this complicated challenge into several smaller subproblems, namely: modeling the receptor protein, predicting the peptide binding site, sampling an initial peptide backbone conformation and the final refinement of the peptide within the receptor binding site. For each of these conceptual stages, we present available tools, approaches, and their reported performance. We summarize with an illustrative example of this process, highlighting the success and current challenges still facing the automated blind modeling of peptide-protein interactions. We believe that the upcoming years will see considerable progress in our ability to create accurate models of peptide-protein interactions, with applications in binding-specificity prediction, rational design of peptide-mediated interactions and the usage of peptides as therapeutic agents.

Sub-angstrom modeling of complexes between flexible peptides and globular proteins

A wide range of regulatory processes in the cell are mediated by flexible peptides that fold upon binding to globular proteins. Computational efforts to model these interactions are hindered by the large number of rotatable bonds in flexible peptides relative to typical ligand molecules, and the fact that different peptides assume different backbone conformations within the same binding site. In this study, we present Rosetta FlexPepDock, a novel tool for refining coarse peptide-protein models that allows significant changes in both peptide backbone and side chains. We obtain high resolution models, often of sub-angstrom backbone quality, over an extensive and general benchmark that is based on a large nonredundant dataset of 89 peptide-protein interactions. Importantly, side chains of known binding motifs are modeled particularly well, typically with atomic accuracy. In addition, our protocol has improved modeling quality for the important application of cross docking to PDZ domains. We anticipate that the ability to create high resolution models for a wide range of peptide-protein complexes will have significant impact on structure-based functional characterization, controlled manipulation of peptide interactions, and on peptide-based drug design.

BriX: a database of protein building blocks for structural analysis, modeling and design

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.

The culprit behind amyloid beta peptide related neurotoxicity in Alzheimer's disease: oligomer size or conformation?

Since the reformulation of the amyloid cascade hypothesis to focus on oligomeric aggregates of amyloid beta as the prime toxic species causing Alzheimer's disease, many researchers refocused on detecting a specific molecular assembly of defined size thatis the main trigger of Alzheimer's disease. The result has been the identification of a host of molecular assemblies containing from two up to a hundred molecules of the amyloid beta peptide, which were all found to impair memory formation in mice. This clearly demonstrates that size is insufficient to define toxicity and peptide conformation has to be taken into account. In this review we discuss the interplay between oligomer size and peptide conformation as the key determinants of the neurotoxicity of the amyloid beta peptide.

VitAL: Viterbi algorithm for de novo peptide design

Background

Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.

Methodology/Principal Findings

A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.

Conclusions/Significance

The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.

PepX: a structural database of non-redundant protein-peptide complexes

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.