Conformational energies and entropies of peptides, and the peptide–protein binding problem

Novel Nuclear Factor-KappaB Targeting Peptide Suppresses β-Amyloid Induced Inflammatory and Apoptotic Responses in Neuronal Cells

In the central nervous system (CNS), activation of the transcription factor nuclear factor-kappa B (NF-κβ) is associated with both neuronal survival and increased vulnerability to apoptosis. The mechanisms underlying these dichotomous effects are attributed to the composition of NF-κΒ dimers. In Alzheimer’s disease (AD), β-amyloid (Aβ) and other aggregates upregulate activation of p65:p50 dimers in CNS cells and enhance transactivation of pathological mediators that cause neuroinflammation and neurodegeneration. Hence selective targeting of activated p65 is an attractive therapeutic strategy for AD. Here we report the design, structural and functional characterization of peptide analogs of a p65 interacting protein, the glucocorticoid induced leucine zipper (GILZ). By virtue of binding the transactivation domain of p65 exposed after release from the inhibitory IκΒ proteins in activated cells, the GILZ analogs can act as highly selective inhibitors of activated p65 with minimal potential for off-target effects.

Functional characterization of a competitive peptide antagonist of p65 in human macrophage-like cells suggests therapeutic potential for chronic inflammation

Glucocorticoid-induced leucine zipper (GILZ) is a glucocorticoid responsive protein that links the nuclear factor-kappa B (NFκB) and the glucocorticoid signaling pathways. Functional and binding studies suggest that the proline-rich region at the carboxy terminus of GILZ binds the p65 subunit of NFκB and suppresses the immunoinflammatory response. A widely-used strategy in the discovery of peptide drugs involves exploitation of the complementary surfaces of naturally occurring binding partners. Previously, we observed that a synthetic peptide (GILZ-P) derived from the proline-rich region of GILZ bound activated p65 and ameliorated experimental encephalomyelitis. Here we characterize the secondary structure of GILZ-P by circular dichroic analysis. GILZ-P adopts an extended polyproline type II helical conformation consistent with the structural conformation commonly observed in interfaces of transient intermolecular interactions. To determine the potential application of GILZ-P in humans, we evaluated the toxicity and efficacy of the peptide drug in mature human macrophage-like THP-1 cells. Treatment with GILZ-P at a wide range of concentrations commonly used for peptide drugs was nontoxic as determined by cell viability and apoptosis assays. Functionally, GILZ-P suppressed proliferation and glutamate secretion by activated macrophages by inhibiting nuclear translocation of p65. Collectively, our data suggest that the GILZ-P has therapeutic potential in chronic CNS diseases where persistent inflammation leads to neurodegeneration such as multiple sclerosis and Alzheimer's disease.

Conformational transitions in the Ramachandran space of amino acids using the dynamic rotational isomeric state (DRIS) model

The dynamic rotational isomeric state model is applied to predict the internal dynamics of the 20 amino acids. Transition rates between rotational isomeric states are calculated from molecular dynamics simulations of Gly-Gly-X-Gly-Gly peptides where X represents one of the 20 amino acids. Predicted relaxation times are in good agreement with fluorescence quenching rate measurements.

Proline rich motifs as drug targets in immune mediated disorders

The current version of the human immunome network consists of nearly 1400 interactions involving approximately 600 proteins. Intermolecular interactions mediated by proline-rich motifs (PRMs) are observed in many facets of the immune response. The proline-rich regions are known to preferentially adopt a polyproline type II helical conformation, an extended structure that facilitates transient intermolecular interactions such as signal transduction, antigen recognition, cell-cell communication and cytoskeletal organization. The propensity of both the side chain and the backbone carbonyls of the polyproline type II helix to participate in the interface interaction makes it an excellent recognition motif. An advantage of such distinct chemical features is that the interactions can be discriminatory even in the absence of high affinities. Indeed, the immune response is mediated by well-orchestrated low-affinity short-duration intermolecular interactions. The proline-rich regions are predominantly localized in the solvent-exposed regions such as the loops, intrinsically disordered regions, or between domains that constitute the intermolecular interface. Peptide mimics of the PRM have been suggested as potential antagonists of intermolecular interactions. In this paper, we discuss novel PRM-mediated interactions in the human immunome that potentially serve as attractive targets for immunomodulation and drug development for inflammatory and autoimmune pathologies.

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.

The structural basis of peptide-protein binding strategies

Peptide-protein interactions are very prevalent, mediating key processes such as signal transduction and protein trafficking. How can peptides overcome the entropic cost involved in switching from an unstructured, flexible peptide to a rigid, well-defined bound structure? A structure-based analysis of peptide-protein interactions unravels that most peptides do not induce conformational changes on their partner upon binding, thus minimizing the entropic cost of binding. Furthermore, peptides display interfaces that are better packed than protein-protein interfaces and contain significantly more hydrogen bonds, mainly those involving the peptide backbone. Additionally, "hot spot" residues contribute most of the binding energy. Finally, peptides tend to bind in the largest pockets available on the protein surface. Our study is based on peptiDB, a new and comprehensive data set of 103 high-resolution peptide-protein complex structures. In addition to improved understanding of peptide-protein interactions, our findings have direct implications for the structural modeling, design, and manipulation of these interactions.