The C-terminal self-binding helical peptide of human estrogen-related receptor γ can be druggably targeted by a novel class of rationally designed peptidic antagonists.

Orphan nuclear estrogen-related receptor γ (ERRγ) has been recognized as a potential therapeutic target for cancer, inflammation and metabolic disorder. The ERRγ contains a regulatory AF2 helical tail linked C-terminally to its ligand-binding domain (LBD), which is a self-binding peptide (SBP) and serves as molecular switch to dynamically regulate the receptor alternation between active and inactive states by binding to and unbinding from the AF2-binding site on ERRγ LBD surface, respectively. Traditional ERRγ modulators are all small-molecule chemical ligands that can be classified into agonists and inverse agonists in terms of their action mechanism; the agonists stabilize the AF2 in ABS site with an agonist conformation, while the inverse agonists lock the AF2 out of the site to largely abolish ERRγ transcriptional activity. Here, a class of ERRγ peptidic antagonists was described to compete with native AF2 for the ABS site, thus blocking the active state of AF2 binding to ERRγ LBD domain. Self-inhibitory peptide was derived from the SBP-covering AF2 region and we expected it can rebind potently to the ABS site by reducing its intrinsic disorder and entropy cost upon the rebinding. Hydrocarbon stapling was employed to do so, which employed an all-hydrocarbon bridge across the [i, i + 4]-anchor residue pair in the N-terminal, middle or C-terminal region of the self-inhibitory peptide. As might be expected, it is revealed that the stapled peptides are good binders of ERRγ LBD domain and can effectively compete with the native AF2 helical tail for ERRγ ABS site, which exhibit a basically similar binding mode with AF2 to the site and form diverse noncovalent interactions with the site, thus conferring stability and specificity to the domain-peptide complexes.

Targeting peptide-mediated interactions in omics.

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.

Machine annealing-guided navigation of antihypertensive food peptide selectivity between human ACE N- and C-domains in structurally interacting diversity space.

Human angiotensin-converting enzyme (ACE) is a well-established druggable target for the treatment of hypertension (HTN), which contains two structurally homologous but functionally distinct N- and C-domains. Selective inhibition of the C-domain primarily contributes to the antihypertensive efficiency and can be exploited as medicinal agents and functional additives for regulating blood pressure with high safety. In this study, we used a machine annealing (MA) strategy to guide the navigation of antihypertensive peptides (AHPs) in structurally interacting diversity space with the two ACE domains based on their crystal/modeled complex structures and an in-house protein-peptide affinity scoring function, aiming to optimize the peptide selectivity for C-domain over N-domain. The strategy generated a panel of theoretically designed AHP hits with a satisfactory C-over-N (C > N) selectivity profile, from which several hits were found to have a good C > N selectivity, which is roughly comparable with or even better than the BPPb, a natural C > N-selective ACE-inhibitory peptide. Structural analysis and comparison of domain-peptide noncovalent interaction patterns revealed that (i) longer peptides (>4 amino aids) generally exhibit stronger selectivity than shorter peptides (<4 amino aids), (ii) peptide sequence can be divided into two, section I (including peptide C-terminal region) and section II (including peptide middle and N-terminal regions); the former contributes to both peptide affinity (primarily) and selectivity (secondarily), while the latter is almost only responsible for peptide selectivity, and (iii) charged/polar amino acids confer to peptide selectivity relative to hydrophobic/nonpolar amino acids (that confer to peptide affinity).

Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys.

Protein-peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity-of-opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date. In this study, we classified PpI specificity into three types: (i) class-I specificity: peptide selectivity for its cognate wild-type protein receptor over the noncognate mutant decoys of this receptor, (ii) class-II specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are homologous with this receptor, and (iii) class-III specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are the cognate receptors of other peptides. We performed affinity and selectivity analysis for the three types of PpI specificity and revealed that the PpIs generally exhibit a moderate or modest specificity; peptide selectivity increases in the order: class-I < class-II < class-III. All the three types of PpI specificity were observed to have no statistically significant correlation with peptide length and hydrophobicity, but the class-I and class-II specificities can be influenced considerably by peptide secondary structures; the high specificity is preferentially associated with ordered structure types as compared to undefined structure types. In addition, the mutation distribution (for class-I specificity), sequence conservation (for class-II specificity), and structural similarity (for class-III specificity) seem also to address effects on peptide selectivity.

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships.

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .

A Structure-based Data Set of Protein-Peptide Affinities and its Nonredundant Benchmark: Potential Applications in Computational Peptidology.

BACKGROUND: Peptides play crucial roles in diverse cellular functions and participate in many biological processes by interacting with a variety of proteins, which have also been exploited as a promising class of therapeutic agents to target druggable proteins over the past decades. Understanding the intrinsic association between the structure and affinity ofprotein-peptide interactions (PpIs) should be considerably valuable for the computational peptidology area, such as guiding protein-peptide docking calculations, developing protein-peptide affinity scoring functions, and designing peptide ligands for specific protein receptors. OBJECTIVE: We attempted to create a data source for relating PpI structure to affinity. METHODS: By exhaustively surveying the whole protein data bank (PDB) database as well as the ontologically enriched literature information, we manually curated a structure-based data set of protein-peptide affinities, PpI[S/A]DS, which assembled over 350 PpI complex samples with both the experimentally measured structure and affinity data. The data set was further reduced to a nonredundant benchmark consisting of 102 culled samples, PpI[S/A]BM, which only selected those of structurally reliable, functionally diverse and evolutionarily nonhomologous. RESULTS: The collected structures were resolved at a high-resolution level with either X-ray crystallography or solution NMR, while the deposited affinities were characterized by dissociation constant, i.e. Kd value, which is a direct biophysical measure of the intermolecular interaction strength between protein and peptide, ranging from subnanomolar to millimolar levels. The PpI samples in the set/benchmark were arbitrarily classified into α-helix, partial α-helix, ß-sheet formed through binding, ß-strand formed through self-folding, mixed, and other irregular ones, totally resulting in six classes according to the secondary structure of their peptide ligands. In addition, we also categorized these PpIs in terms of their biological function and binding behavior. CONCLUSION: The PpI[S/A]DS set and PpI[S/A]BM benchmark can be considered a valuable data source in the computational peptidology community, aiming to relate the affinity to structure for PpIs.