Identification of Secondary Binding Sites on Protein Surfaces for Rational Elaboration of Synthetic Protein Mimics

An update patent review of MDM2-p53 interaction inhibitors (2019-2023)

INTRODUCTION

The activity of the major tumor suppressor protein p53 is disrupted in nearly all human cancer types, either by mutations in TP53 gene or by overexpression of its negative regulator, Mouse Double Minute 2 (MDM2). The release of p53 from MDM2 and its homolog MDM4 with inhibitors based on different chemistries opened up a prospect for a broad, non-genotoxic anticancer therapy.

AREAS COVERED

This article reviews the patents and patent applications between years 2019 and 2023 in the field of MDM2-p53 interaction inhibitors. The newly reported molecules searched in Espacenet, Google Patents, and PubMed were grouped into five general categories: compounds having single-ring, multi-ring, or spiro-oxindole scaffolds, peptide derivatives, and proteolysis-targeting chimeras (PROTACs). The article also presents the progress of MDM2 antagonists of various structures in recruiting or completed cancer clinical trials.

EXPERT OPINION

Despite 20 years of intensive studies after the discovery of the first-in-class small-molecule inhibitor, Nutlin-3, no drugs targeting MDM2-p53 interaction have reached the market. Nevertheless, more than 10 compounds are still being evaluated in clinics, both as standalone drugs and in combinations with other targeted therapies or standard chemotherapy agents, including two inhibitors in phase 3 studies and two compounds granted orphan-drug/fast-track designation by the FDA.

Structural Adaptation of Secondary p53 Binding Sites on MDM2 and MDMX

The thermodynamics of secondary p53 binding sites on MDM2 and MDMX were evaluated using p53 peptides containing residues 16-29, 17-35, and 1-73. All the peptides had large, negative heat capacity (ΔCp), consistent with the burial of p53 residues F19, W23, and L26 in the primary binding sites of MDM2 and MDMX. MDMX has a higher affinity and more negative ΔCp than MDM2 for p5317-35, which is due to MDMX stabilization and not additional interactions with the secondary binding site. ΔCp measurements show binding to the secondary site is inhibited by the disordered tails of MDM2 for WT p53 but not a more helical mutant where proline 27 is changed to alanine. This result is supported by all-atom molecular dynamics simulations showing that p53 residues 30-35 turn away from the disordered tails of MDM2 in P27A17-35 and make direct contact with this region in p5317-35. Molecular dynamics simulations also suggest that an intramolecular methionine-aromatic motif found in both MDM2 and MDMX structurally adapts to support multiple p53 binding modes with the secondary site. ΔCp measurements also show that tighter binding of the P27A mutant to MDM2 and MDMX is due to increased helicity, which reduces the energetic penalty associated with coupled folding and binding. Our results will facilitate the design of selective p53 inhibitors for MDM2 and MDMX.

Development of an α-Klotho Recognizing High-Affinity Peptide Probe from In-Solution Enrichment

The kidney, parathyroid gland, and choroid plexus express the aging-related transmembrane protein α-Klotho, a coreceptor of the fibroblast growth factor 23 (FGF23) receptor complex. Reduced α-Klotho levels are correlated with chronic kidney disease and other age-related diseases, wherein they are released from membranes into circulation. Klotho's potential physiological action as a hormone is of current scientific interest. Part of the challenges associated with advancing these studies, however, has been the long-standing difficulty in detecting soluble α-Klotho in biofluids. Here, we describe the discovery of peptides that recognize α-Klotho with high affinity and selectivity by applying in-solution size-exclusion-based affinity selection-mass spectrometry (AS-MS). After two rounds of AS-MS and subsequent N-terminal modifications, the peptides improved their binding affinity to α-Klotho by approximately 2300-fold compared to the reported starting peptide Pep-10, previously designed based on the C-terminal region of FGF23. The lead peptide binders were shown to enrich α-Klotho from cell lysates and to label α-Klotho in kidney cells. Our results further support the utility of in-solution, label-free AS-MS protocols to discover peptide-based binders to target proteins of interest with high affinity and selectivity, resulting in functional probes for biological studies.

Discovery of reactive peptide inhibitors of human papillomavirus oncoprotein E6

Human papillomavirus (HPV) infections account for nearly all cervical cancer cases, which is the fourth most common cancer in women worldwide. High-risk variants, including HPV16, drive tumorigenesis in part by promoting the degradation of the tumor suppressor p53. This degradation is mediated by the HPV early protein 6 (E6), which recruits the E3 ubiquitin ligase E6AP and redirects its activity towards ubiquitinating p53. Targeting the protein interaction interface between HPV E6 and E6AP is a promising modality to mitigate HPV-mediated degradation of p53. In this study, we designed a covalent peptide inhibitor, termed reactide, that mimics the E6AP LXXLL binding motif by selectively targeting cysteine 58 in HPV16 E6 with quantitative conversion. This reactide provides a starting point in the development of covalent peptidomimetic inhibitors for intervention against HPV-driven cancers.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Molecular Complementarity of Proteomimetic Materials for Target-Specific Recognition and Recognition-Mediated Complex Functions

As biomolecules essential for sustaining life, proteins are generated from long chains of 20 different α-amino acids that are folded into unique 3D structures. In particular, many proteins have molecular recognition functions owing to their binding pockets, which have complementary shapes, charges, and polarities for specific targets, making these biopolymers unique and highly valuable for biomedical and biocatalytic applications. Based on the understanding of protein structures and microenvironments, molecular complementarity can be exhibited by synthesizable and modifiable materials. This has prompted researchers to explore the proteomimetic potentials of a diverse range of materials, including biologically available peptides and oligonucleotides, synthetic supramolecules, inorganic molecules, and related coordination networks. To fully resemble a protein, proteomimetic materials perform the molecular recognition to mediate complex molecular functions, such as allosteric regulation, signal transduction, enzymatic reactions, and stimuli-responsive motions; this can also expand the landscape of their potential bio-applications. This review focuses on the recognitive aspects of proteomimetic designs derived for individual materials and their conformations. Recent progress provides insights to help guide the development of advanced protein mimicry with material heterogeneity, design modularity, and tailored functionality. The perspectives and challenges of current proteomimetic designs and tools are also discussed in relation to future applications.

Design of Protein Segments and Peptides for Binding to Protein Targets

Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.

Peptide Tethering: Pocket-Directed Fragment Screening for Peptidomimetic Inhibitor Discovery

Constrained peptides have proven to be a rich source of ligands for protein surfaces, but are often limited in their binding potency. Deployment of nonnatural side chains that access unoccupied crevices on the receptor surface offers a potential avenue to enhance binding affinity. We recently described a computational approach to create topographic maps of protein surfaces to guide the design of nonnatural side chains [J. Am. Chem. Soc. 2017, 139, 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.