On-Demand Thio-Succinimide Hydrolysis for the Assembly of Stable Protein-Protein Conjugates.

Chemical post-translational protein-protein conjugation is an important technique with growing applications in biotechnology and pharmaceutical research. Maleimides represent one of the most widely employed bioconjugation reagents. However, challenges associated with the instability of first- and second-generation maleimide technologies are yet to be fully addressed. We report the development of a novel class of maleimide reagents that can undergo on-demand ring-opening hydrolysis of the resulting thio-succinimide. This strategy enables rapid post-translational assembly of protein-protein conjugates. Thio-succinimide hydrolysis, triggered upon application of chemical, photochemical, or enzymatic stimuli, allowed homobifunctional bis-maleimide reagents to be applied in the production of stable protein-protein conjugates, with complete temporal control. Bivalent and bispecific protein-protein dimers constructed from small binders targeting antigens of oncological importance, PD-L1 and HER2, were generated with high purity, stability, and improved functionality compared to monomeric building blocks. The modularity of the approach was demonstrated through elaboration of the linker moiety through a bioorthogonal propargyl handle to produce protein-protein-fluorophore conjugates. Furthermore, extending the functionality of the homobifunctional reagents by temporarily masking reactive thiols included in the linker allowed the assembly of higher order trimeric and tetrameric single-domain antibody conjugates. The potential for the approach to be extended to proteins of greater biochemical complexity was demonstrated in the production of immunoglobulin single-domain antibody conjugates. On-demand control of thio-succinimide hydrolysis combined with the facile assembly of chemically defined homo- and heterodimers constitutes an important expansion of the chemical methods available for generating stable protein-protein conjugates.

Gold(III)-Induced Amide Bond Cleavage In Vivo: A Dual Release Strategy via π-Acid Mediated Allyl Substitution.

Selective cleavage of amide bonds holds prominent significance by facilitating precise manipulation of biomolecules, with implications spanning from basic research to therapeutic interventions. However, achieving selective cleavage of amide bonds via mild synthetic chemistry routes poses a critical challenge. Here, we report a novel amide bond-cleavage reaction triggered by Na[AuCl4] in mild aqueous conditions, where a crucial cyclization step leads to the formation of a 5-membered ring intermediate that rapidly hydrolyses to release the free amine in high yields. Notably, the reaction exhibits remarkable site-specificity to cleave peptide bonds at the C-terminus of allyl-glycine. The strategic introduction of a leaving group at the allyl position facilitated a dual-release approach through π-acid catalyzed substitution. This reaction was employed for the targeted release of the cytotoxic drug monomethyl auristatin E in combination with an antibody-drug conjugate in cancer cells. Finally, Au-mediated prodrug activation was shown in a colorectal zebrafish xenograft model, leading to a significant increase in apoptosis and tumor shrinkage. Our findings reveal a novel metal-based cleavable reaction expanding the utility of Au complexes beyond catalysis to encompass bond-cleavage reactions for cancer therapy.

Design and Evaluation of PROTACs Targeting Acyl Protein Thioesterase 1.

PROTAC linker design remains mostly an empirical task. We employed the PRosettaC computational software in the design of sulfonyl-fluoride-based PROTACs targeting acyl protein thioesterase 1 (APT1). The software efficiently generated ternary complex models from empirically-designed PROTACs and suggested alkyl linkers to be the preferred type of linker to target APT1. Western blotting analysis revealed efficient degradation of APT1 and activity-based protein profiling showed remarkable selectivity of an alkyl linker-based PROTAC amongst serine hydrolases. Collectively, our data suggests that combining PRosettaC and chemoproteomics can effectively assist in triaging PROTACs for synthesis and providing early data on their potency and selectivity.

Cathepsin B Processing Is Required for the In Vivo Efficacy of Albumin-Drug Conjugates.

Targeted drug delivery approaches that selectively and preferentially deliver therapeutic agents to specific tissues are of great interest for safer and more effective pharmaceutical treatments. We investigated whether cathepsin B cleavage of a valine-citrulline [VC(S)]-containing linker is required for the release of monomethyl auristatin E (MMAE) from albumin-drug conjugates. In this study, we used an engineered version of human serum albumin, Veltis High Binder II (HBII), which has enhanced binding to the neonatal Fc (fragment crystallizable) receptor (FcRn) to improve drug release upon binding and FcRn-mediated recycling. The linker-payload was conjugated to cysteine 34 of albumin using a carbonylacrylic (caa) reagent which produced homogeneous and plasma stable conjugates that retained FcRn binding. Two caa-linker-MMAE reagents were synthesized─one with a cleavable [VC(S)] linker and one with a noncleavable [VC(R)] linker─to question whether protease-mediated cleavage is needed for MMAE release. Our findings demonstrate that cathepsin B is required to achieve efficient and selective antitumor activity. The conjugates equipped with the cleavable [VC(S)] linker had potent antitumor activity in vivo facilitated by the release of free MMAE upon FcRn binding and internalization. In addition to the pronounced antitumor activity of the albumin conjugates in vivo, we also demonstrated their preferable tumor biodistribution and biocompatibility with no associated toxicity or side effects. These results suggest that the use of engineered albumins with high FcRn binding combined with protease cleavable linkers is an efficient strategy to target delivery of drugs to solid tumors.

Small Molecule RNA Degraders.

RNA is a central molecule in life, involved in a plethora of biological processes and playing a key role in many diseases. Targeting RNA emerges as a significant endeavor in drug discovery, diverging from conventional protein-centric approaches to tackle various pathologies. Whilst identifying small molecules that bind to specific RNA regions is the first step, the abundance of non-functional RNA segments renders many interactions biologically inert. Consequently, small molecule binding does not necessarily meet stringent criteria for clinical translation, calling for solutions to push the field forward. Converting RNA-binders into RNA-degraders presents a promising avenue to enhance RNA-targeting. This mini-review outlines strategies and exemplars wherein simple small molecule RNA binders are reprogrammed into active degraders through the linkage of functional groups. These approaches encompass mechanisms that induce degradation via endogenous enzymes, termed RIBOTACs, as well as those with functional moieties acting autonomously to degrade RNA. Through this exploration, we aim to offer insights into advancing RNA-targeted therapeutic strategies.

Homogeneous multi-payload antibody-drug conjugates.

Many systemic cancer chemotherapies comprise a combination of drugs, yet all clinically used antibody-drug conjugates (ADCs) contain a single-drug payload. These combination regimens improve treatment outcomes by producing synergistic anticancer effects and slowing the development of drug-resistant cell populations. In an attempt to replicate these regimens and improve the efficacy of targeted therapy, the field of ADCs has moved towards developing techniques that allow for multiple unique payloads to be attached to a single antibody molecule with high homogeneity. However, the methods for generating such constructs-homogeneous multi-payload ADCs-are both numerous and complex owing to the plethora of reactive functional groups that make up the surface of an antibody. Here, by summarizing and comparing the methods of both single- and multi-payload ADC generation and their key preclinical and clinical results, we provide a timely overview of this relatively new area of research. The methods discussed range from branched linker installation to the incorporation of unnatural amino acids, with a generalized comparison tool of the most promising modification strategies also provided. Finally, the successes and challenges of this rapidly growing field are critically evaluated, and from this, future areas of research and development are proposed.

Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms.

Machine learning (ML) models have made inroads into chemical sciences, with optimization of chemical reactions and prediction of biologically active molecules being prime examples thereof. These models excel where physical experiments are expensive or time-consuming, for example, due to large scales or the need for materials that are difficult to obtain. Studies of natural products suffer from these issues─this class of small molecules is known for its wealth of structural diversity and wide-ranging biological activities, but their investigation is hindered by poor synthetic accessibility and lack of scalability. To facilitate the evaluation of these molecules, we designed ML models that predict which natural products can interact with a particular target or a relevant pathway. Here, we focused on discovering natural products that are capable of modulating the 5-lipoxygenase (5-LO) pathway that plays key roles in lipid signaling and inflammation. These computational approaches led to the identification of nine natural products that either directly inhibit the activity of the 5-LO enzyme or affect the cellular 5-LO pathway. Further investigation of one of these molecules, deltonin, led us to discover a new cell-type-selective mechanism of action. Our ML approach helped deorphanize natural products as well as shed light on their mechanisms and can be broadly applied to other use cases in chemical biology.

Structure-Guided Approach for the Development of MUC1-Glycopeptide-Based Cancer Vaccines with Predictable Responses.

Mucin-1 (MUC1) glycopeptides are exceptional candidates for potential cancer vaccines. However, their autoantigenic nature often results in a weak immune response. To overcome this drawback, we carefully engineered synthetic antigens with precise chemical modifications. To be effective and stimulate an anti-MUC1 response, artificial antigens must mimic the conformational dynamics of natural antigens in solution and have an equivalent or higher binding affinity to anti-MUC1 antibodies than their natural counterparts. As a proof of concept, we have developed a glycopeptide that contains noncanonical amino acid (2S,3R)-3-hydroxynorvaline. The unnatural antigen fulfills these two properties and effectively mimics the threonine-derived antigen. On the one hand, conformational analysis in water shows that this surrogate explores a landscape similar to that of the natural variant. On the other hand, the presence of an additional methylene group in the side chain of this analog compared to the threonine residue enhances a CH/π interaction in the antigen/antibody complex. Despite an enthalpy-entropy balance, this synthetic glycopeptide has a binding affinity slightly higher than that of its natural counterpart. When conjugated with gold nanoparticles, the vaccine candidate stimulates the formation of specific anti-MUC1 IgG antibodies in mice and shows efficacy comparable to that of the natural derivative. The antibodies also exhibit cross-reactivity to selectively target, for example, human breast cancer cells. This investigation relied on numerous analytical (e.g., NMR spectroscopy and X-ray crystallography) and biophysical techniques and molecular dynamics simulations to characterize the antigen-antibody interactions. This workflow streamlines the synthetic process, saves time, and reduces the need for extensive, animal-intensive immunization procedures. These advances underscore the promise of structure-based rational design in the advance of cancer vaccine development.