Protein-Based Degraders: From Chemical Biology Tools to Neo-Therapeutics

The nascent field of targeted protein degradation (TPD) could revolutionize biomedicine due to the ability of degrader molecules to selectively modulate disease-relevant proteins. A key limitation to the broad application of TPD is its dependence on small-molecule ligands to target proteins of interest. This leaves unstructured proteins or those lacking defined cavities for small-molecule binding out of the scope of many TPD technologies. The use of proteins, peptides, and nucleic acids (otherwise known as "biologics") as the protein-targeting moieties in degraders addresses this limitation. In the following sections, we provide a comprehensive and critical review of studies that have used proteins and peptides to mediate the degradation and hence the functional control of otherwise challenging disease-relevant protein targets. We describe existing platforms for protein/peptide-based ligand identification and the drug delivery systems that might be exploited for the delivery of biologic-based degraders. Throughout the Review, we underscore the successes, challenges, and opportunities of using protein-based degraders as chemical biology tools to spur discoveries, elucidate mechanisms, and act as a new therapeutic modality.

Immunopeptides: immunomodulatory strategies and prospects for ocular immunity applications

Immunopeptides have low toxicity, low immunogenicity and targeting, and broad application prospects in drug delivery and assembly, which are diverse in application strategies and drug combinations. Immunopeptides are particularly important for regulating ocular immune homeostasis, as the eye is an immune-privileged organ. Immunopeptides have advantages in adaptive immunity and innate immunity, treating eye immune-related diseases by regulating T cells, B cells, immune checkpoints, and cytokines. This article summarizes the application strategies of immunopeptides in innate immunity and adaptive immunity, including autoimmunity, infection, vaccine strategies, and tumors. Furthermore, it focuses on the mechanisms of immunopeptides in mediating ocular immunity (autoimmune diseases, inflammatory storms, and tumors). Moreover, it reviews immunopeptides' application strategies and the therapeutic potential of immunopeptides in the eye. We expect the immune peptide to get attention in treating eye diseases and to provide a direction for eye disease immune peptide research.

Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .

Targeting Peptides: The New Generation of Targeted Drug Delivery Systems

Peptides can act as targeting molecules, analogously to oligonucleotide aptamers and antibodies. They are particularly efficient in terms of production and stability in physiological environments; in recent years, they have been increasingly studied as targeting agents for several diseases, from tumors to central nervous system disorders, also thanks to the ability of some of them to cross the blood-brain barrier. In this review, we will describe the techniques employed for their experimental and in silico design, as well as their possible applications. We will also discuss advancements in their formulation and chemical modifications that make them even more stable and effective. Finally, we will discuss how their use could effectively help to overcome various physiological problems and improve existing treatments.

Synthesis of Unnatural α-Amino Acid Derivatives via Photoredox Activation of Inert C(sp3)-H Bonds

The synthesis of unnatural, tertiary amino acids is a challenging task. While decarboxylation-radical addition has been an important strategy for their formation, the use of alkyl radicals from C(sp³)-H bonds has not been fully explored. Herein, we report a photocatalytic protocol for the synthesis of unnatural α-amino esters employing abundant alkanes and imines retaining full atom economy. When this method is applied, several amino acid derivatives are synthesized in moderate to good yields.

Submonomer synthesis of sequence defined peptoids with diverse side-chains

Peptoids are a diverse family of sequence-defined oligomers of N-substituted glycine monomers, that can be readily accessed by the solid-phase submonomer synthesis method. Due to the versatility and efficiency of this chemistry, and the easy access to hundreds of potential monomers, there is an enormous potential sequence space that can be explored. This has enabled researchers from many different fields to custom-design peptoid sequences tailored to a wide variety of problems in biomedicine, nanoscience and polymer science. Here we provide detailed protocols for the synthesis of peptoids, using optimized protocols that can be performed by non-chemists. The submonomer method is fully compatible with Fmoc-peptide synthesis conditions, so the method is readily automated on existing automated peptide synthesizers using protocols provided here. Although the submonomer synthesis for peptoids is well established, there are special considerations required in order to access many of the most useful and desirable sidechains. Here we provide methods to include most of the amino-acid-like side chains, some of the most important non-natural monomer classes, as well as the creation of peptoid conjugates and peptide-peptoid hybrids.

Impact of non-proteinogenic amino acids in the discovery and development of peptide therapeutics

With the development of modern chemistry and biology, non-proteinogenic amino acids (NPAAs) have become a powerful tool for developing peptide-based drug candidates. Drug-like properties of peptidic medicines, due to the smaller size and simpler structure compared to large proteins, can be changed fundamentally by introducing NPAAs in its sequence. While peptides composed of natural amino acids can be used as drug candidates, the majority have shown to be less stable in biological conditions. The impact of NPAA incorporation can be extremely beneficial in improving the stability, potency, permeability, and bioavailability of peptide-based therapies. Conversely, undesired effects such as toxicity or immunogenicity should also be considered. The impact of NPAAs in the development of peptide-based therapeutics is reviewed in this article. Further, numerous examples of peptides containing NPAAs are presented to highlight the ongoing development in peptide-based therapeutics.

Bringing a Molecular Plus One: Synergistic Binding Creates Guest-Mediated Three-Component Complexes

C_ethyl-2-methylresorcinarene (A), pyridine (B), and a set of 10 carboxylic acids (Cn) associate to form A·B·Cn ternary assemblies with 1:1:1 stoichiometry, representing a useful class of ternary systems where the guest mediates complex formation between the host and a third component. Although individually weak in solution, the combined strength of the multiple noncovalent interactions organizes the complexes even in a highly hydrogen-bond competing methanol solution, as explored by both experimental and computational methods. The interactions between A·B and Cn are dependent on the pK_a values of carboxylic acids. The weak interactions between A and C further reinforce the interactions between A and B, demonstrating positive cooperativity. Our results reveal that the two-component system such as that formed by A and B can form the basis for the development of specific sensors for the molecular recognition of carboxylic acids.

Predicting the Affinity of Peptides to Major Histocompatibility Complex Class II by Scoring Molecular Dynamics Simulations

Predicting the binding affinity of peptides able to interact with major histocompatibility complex (MHC) molecules is a priority for researchers working in the identification of novel vaccines candidates. Most available approaches are based on the analysis of the sequence of peptides of known experimental affinity. However, for MHC class II receptors, these approaches are not very accurate, due to the intrinsic flexibility of the complex. To overcome these limitations, we propose to estimate the binding affinity of peptides bound to an MHC class II by averaging the score of the configurations from finite-temperature molecular dynamics simulations. The score is estimated for 18 different scoring functions, and we explored the optimal manner for combining them. To test the predictions, we considered eight peptides of known binding affinity. We found that six scoring functions correlate with the experimental ranking of the peptides significantly better than the others. We then assessed a set of techniques for combining the scoring functions by linear regression and logistic regression. We obtained a maximum accuracy of 82% for the predicted sign of the binding affinity using a logistic regression with optimized weights. These results are potentially useful to improve the reliability of in silico protocols to design high-affinity binding peptides for MHC class II receptors.