Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry

Structural Basis of Conformational Dynamics in the PROTAC-Induced Protein Degradation

Pronounced conformational dynamics is unveiled upon analyzing multiple crystal structures of the same proteins recruited to the same E3 ligases by PROTACs, and yet, is largely permissive for targeted protein degradation due to the intrinsic mobility of E3 assemblies creating a large ubiquitylation zone. Mathematical modelling of ternary dynamics on ubiquitylation probability confirms the experimental finding that ternary complex rigidification need not correlate with enhanced protein degradation. Salt bridges are found to prevail in the PROTAC-induced ternary complexes, and may contribute to a positive cooperativity and prolonged half-life. The analysis highlights the importance of presenting lysines close to the active site of the E2 enzyme while constraining ternary dynamics in PROTAC design to achieve high degradation efficiency.

Targeted Protein Degradation: Current and Emerging Approaches for E3 Ligase Deconvolution

Targeted protein degradation (TPD), including the use of proteolysis-targeting chimeras (PROTACs) and molecular glue degraders (MGDs) to degrade proteins, is an emerging strategy to develop novel therapies for cancer and beyond. PROTACs or MGDs function by inducing the proximity between an E3 ligase and a protein of interest (POI), leading to ubiquitination and consequent proteasomal degradation of the POI. Notably, one major issue in TPD is the lack of ligandable E3 ligases, as current studies predominantly use CUL4CRBN and CUL2VHL. The TPD community is seeking to expand the landscape of ligandable E3 ligases, but most discoveries rely on phenotypic screens or serendipity, necessitating systematic target deconvolution. Here, we examine and discuss both current and emerging E3 ligase deconvolution approaches for degraders discovered from phenotypic screens or monovalent glue chemistry campaigns, highlighting future prospects for identifying more ligandable E3 ligases.

PROTAC-induced Protein Functional Dynamics in Targeted Protein Degradation

PROteolysis TArgeting Chimeras (PROTACs) are small molecules that induce target protein degradation via the ubiquitin proteasome system. PROTACs recruit the target protein and E3 ligase; a critical first step is forming a ternary complex. However, while the formation a ternary complex is crucial, it may not always guarantee successful protein degradation. The dynamics of the PROTAC induced degradation complex play a key role in ubiquitination and subsequent degradation. In this study, we computationally modelled protein complex structures and dynamics associated with a series of PROTACs featuring different linkers to investigate why these PROTACs, all of which formed ternary complexes with Cereblon (CRBN) E3 ligase and the target protein bromodomain containing protein 4 (BRD4BD1), exhibited varying degrees of degradation potency. We constructed the degradation machinery complexes with Culling Ring Ligase 4A (CRL4A) E3 ligase scaffolds. Through atomistic molecular dynamics simulations, we illustrated how PROTAC dependent protein dynamics facilitate the arrangement of surface lysine residues of BRD4BD1 into the catalytic pocket of E2/ubiquitin for ubiquitination. Despite featuring identical warheads in this PROTAC series, the linkers were found to affect the residue interaction networks, and thus governing the essential motions of the entire degradation machine for ubiquitination. These findings offer a dynamic perspective on ligand induced protein degradation, providing insights to guide future PROTAC design endeavors.

Design, synthesis, in silico and biological evaluation of new indole based oxadiazole derivatives targeting estrogen receptor alpha

A series of new indole-oxadiazole derivatives was designed and synthesized to develop potential anti-breast cancer agents. The compounds exhibited significant inhibitory activity with IC50 values ranging from 1.78 to 19.74 μM against ER-positive human breast cancer (BC) cell lines T-47D and MCF-7. Among them, compounds (5a, 5c, 5e-5h, 5j-5o) displayed superior activity against ER-α dominant (ratio of ER-α/ER-β is 9/1) T-47D cells compared to the standard drug bazedoxifene (IC50 = 12.78 ± 0.92 μM). Compounds 5c and 5o exhibited remarkable anti-proliferative activity with IC50 values of 3.24 ± 0.46 and 1.72 ± 1.67 μM against T-47D cells, respectively. Further, compound 5o manifested 1589-fold higher ER-α binding affinity (213.4 pM) relative to bazedoxifene (339.2 nM) in a competitive ER-α binding assay, while compound 5c showed a binding affinity of 446.6 nM. The Western blot analysis proved that both compounds influenced the ER-α protein's expression, impeding its subsequent transactivation and signalling pathway within T-47D cells. Additionally, a molecular docking study suggests that compounds 5c and 5o bind in such a fashion that induces conformational changes in the protein, culminating in their antagonistic effect. Also, pharmacokinetic profiles showed that all compounds have drug-like properties. Further, molecular dynamic (MD) simulations and density functional theory (DFT) analysis confirmed the stability, conformational behaviour, reactivity, and biological feasibility of compounds 5c and 5o. In conclusion, based on our findings, compounds 5c and 5o, which exhibit significant ER-α antagonistic activity, can act as potential lead compounds for developing anti-breast cancer agents.

Lysineless HiBiT and NanoLuc Tagging Systems as Alternative Tools Monitoring Targeted Protein Degradation

Target protein degradation (TPD) has emerged as a revolutionary approach in drug discovery, leveraging the cell's intrinsic machinery to selectively degrade disease-associated proteins. Proteolysis-Targeting Chimeras (PROTACs) exemplify this strategy, exploiting heterobifunctional molecules to induce ubiquitination and subsequent degradation of target proteins. The clinical advancement of PROTACs underscores their potential in therapeutic intervention, with numerous projects progressing through clinical stages. However, monitoring subtle changes in protein abundance induced by TPD molecules demands highly sensitive assays. Nano-luciferase (nLuc) fusion proteins, or the NanoBiT technology derived from it, offer a robust screening platform due to their high sensitivity and stability. Despite these advantages, concerns have arisen regarding potential degradation artifacts introduced by tagging systems due to the presence of lysine residues on them, prompting the development of alternative tools. In this study, we introduce HiBiT-RR and nLuc K0 , variants devoid of lysine residues, to mitigate such artifacts. Our findings demonstrate that HiBiT-RR maintains similar sensitivity and binding affinity with the original HiBiT. Moreover, the comparison between nLuc WT and nLuc K0 constructs reveals variations in degradation patterns induced by certain PROTAC molecules, emphasizing the importance of choosing appropriate tagging systems to ensure the reliability of experimental outcomes in studying protein degradation processes.

Design, synthesis, in vitro and in silico evaluation of indole-based tetrazole derivatives as putative anti-breast cancer agents

A series of new indole-tetrazole derivatives were designed and synthesized to develop potential anti-breast cancer agents. The compounds exhibited in vitro anti-proliferative activity against ER-α positive T-47D (IC50 = 3.82-24.43 μM), MCF-7 (IC50 = 3.08-22.65 μM), and ER-α negative MDA-MB-231 (IC50 = 7.69-19.4 μM) human breast cancer cell lines. Compounds 5d and 5f displayed significant anti-proliferative activity compared to bazedoxifene (IC50 = 14.23 ± 0.68 μM), with IC50 values of 10.00 ± 0.59 and 3.83 ± 0.74 μM, respectively, against the ER-α dominant T-47D cell line. Also, both compounds showed non-significant cytotoxicity against normal cells HEK-293. Further, the ER-α binding affinity of 5d and 5f was assessed through a fluorescence polarization-based competitive binding assay, where 5d and 5f have shown significant binding with IC50 = 5.826 and 110.6 nM, respectively, as compared to the standard drug bazedoxifene (IC50 = 339.2 nM). Western blot analysis confirmed that compound 5d reduced ER-α protein expression in T-47D cells, hindering its transactivation and signalling pathways. Additionally, a molecular docking study suggests that compounds 5d and 5f bind in such a fashion that induces conformational changes in the protein, culminating in their antagonistic effect. Pharmacokinetic profiles showed that the compounds possessed drug-like properties. Furthermore, molecular dynamics simulation studies establish the dynamic stability and conformational behaviour of the ER-α protein and ligand complex of both compounds. Additionally, 5d and 5f ensure biological feasibility as per their DFT analysis through HOMO-LUMO energy gap analysis. In conclusion, compounds 5d and 5f, exhibiting significant ER-α antagonistic activity, can act as potential lead compounds for anti-breast cancer therapies.

Network pharmacology, molecular docking, and molecular dynamics simulation analysis reveal the molecular mechanism of halociline against gastric cancer

Halociline, a derivative of alkaloids, was isolated from the marine fungus Penicillium griseofulvum by our group. This remarkable compound exhibits promising antineoplastic activity, yet the precise molecular mechanisms underlying its anticancer properties remain enigmatic. To unravel these mechanisms, we employed an integrated approach of network pharmacology analysis, molecular docking simulations, and molecular dynamics simulations to explore halociline therapeutic targets for gastric cancer. The data from network pharmacology indicate that halociline targets MAPK1, MMP-9, and PIK3CA in gastric cancer cells, potentially mediated by diverse pathways including cancer, lipid metabolism, atherosclerosis, and EGFR tyrosine kinase inhibitor resistance. Notably, molecular docking and dynamics simulations revealed a high affinity between halociline and these targets, with free binding energies (ΔEtotal) of - 20.28, - 27.94, and - 25.97 kcal/mol for MAPK1, MMP-9, and PIK3CA, respectively. This study offers valuable insights into the potential molecular mechanism of halociline's inhibition of gastric cancer cells and serves as a valuable reference for future basic research efforts.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

How Robust Is the Ligand Binding Transition State?

For many drug targets, it has been shown that the kinetics of drug binding (e.g., on rate and off rate) is more predictive of drug efficacy than thermodynamic quantities alone. This motivates the development of predictive computational models that can be used to optimize compounds on the basis of their kinetics. The structural details underpinning these computational models are found not only in the bound state but also in the short-lived ligand binding transition states. Although transition states cannot be directly observed experimentally due to their extremely short lifetimes, recent successes have demonstrated that modeling the ligand binding transition state is possible with the help of enhanced sampling molecular dynamics methods. Previously, we generated unbinding paths for an inhibitor of soluble epoxide hydrolase (sEH) with a residence time of 11 min. Here, we computationally modeled unbinding events with the weighted ensemble method REVO (resampling of ensembles by variation optimization) for five additional inhibitors of sEH with residence times ranging from 14.25 to 31.75 min, with average prediction accuracy within an order of magnitude. The unbinding ensembles are analyzed in detail, focusing on features of the ligand binding transition state ensembles (TSEs). We find that ligands with similar bound poses can show significant differences in their ligand binding TSEs, in terms of their spatial distribution and protein-ligand interactions. However, we also find similarities across the TSEs when examining more general features such as ligand degrees of freedom. Together these findings show significant challenges for rational, kinetics-based drug design.

Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

The potential of cross-linking mass spectrometry in the development of protein-protein interaction modulators

Cross-linking mass spectrometry (XL-MS) can provide a wealth of information on endogenous protein-protein interaction (PPI) networks and protein binding interfaces. These features make XL-MS an attractive tool to support the development of PPI-targeting drugs. Though not yet widely used, applications of XL-MS to drug characterization are beginning to emerge. Here, we compare XL-MS to established structural proteomics methods in drug research, discuss the current state and remaining challenges of XL-MS technology, and provide a perspective on the future role XL-MS can play in drug development, with a particular emphasis on PPI modulators.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Identification of 1,3,4-oxadiazoles as tubulin-targeted anticancer agents: a combined field-based 3D-QSAR, pharmacophore model-based virtual screening, molecular docking, molecular dynamics simulation, and density functional theory calculation approach

Cancer is one of the most prominent causes of death worldwide and tubulin is a crucial protein of cytoskeleton that maintains essential cellular functions including cell division as well as cell signalling, that makes an attractive drug target for cancer drug development. 1,3,4-oxadiazoles disrupt microtubule causing G2-M phase cell cycle arrest and provide anti-proliferative effect. In this study, field-based 3D-QSAR models were developed using 62 bioactive anti-tubulin 1,3,4-oxadiazoles. The best model characterized by PLS factor 7 was rigorously validated using various statistical parameters. Generated 3D-QSAR model having high degree of confidence showed favourable and unfavourable contours around 1,3,4-oxadiazole core that assisted in defining proper spatial positioning of desired functional groups for better bioactivity. A five featured pharmacophore model (AAHHR_1) was developed using same ligand library and validated through enrichment analysis (BEDROC160.9 value = 0.59, Average EF 1% = 27.05, and AUC = 0.74). Total 30,212 derivatives of 1,3,4-oxadiazole obtained from PubChem database was prefiltered through validated pharmacophore model and docked in XP mode on binding cavity of tubulin protein (PDB code: 1SA0) which led into the identification of 11 HITs having docking scores between -7.530 and -9.719 kcal/mol while the reference compound Colchicine exerted docking score of -7.046 kcal/mol. Following the analysis of MM-GBSA and ADME studies, HIT1 and HIT4 emerged as the two promising hits. To verify their thermodynamic stability at the target site, molecular dynamic simulations were carried out. Both HITs were further subjected to DFT analysis to determine their HOMO-LUMO energy gap for ensuring their biological feasibility. Finally, molecular docking based structural exploration for 1,3,4-oxadiazoles to set up a lead of Formula I for further advancements of tubulin polymerization inhibitors as anti-cancer agents.Communicated by Ramaswamy H. Sarma.

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes

Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.

Adrenomedullin 2/intermedin is a slow off-rate, long-acting endogenous agonist of the adrenomedullin2 G protein-coupled receptor

Adrenomedullin 2/intermedin (AM2/IMD), adrenomedullin (AM), and calcitonin gene-related peptide (CGRP) have signaling functions in the cardiovascular, lymphatic, and nervous systems by activating three heterodimeric receptors comprised of the class B GPCR CLR and a RAMP1, -2, or -3 modulatory subunit. CGRP and AM prefer the RAMP1 and RAMP2/3 complexes, respectively, whereas AM2/IMD is thought to be relatively non-selective. Accordingly, AM2/IMD exhibits overlapping actions with CGRP and AM, so the rationale for this third agonist for the CLR-RAMP complexes is unclear. Here, we report that AM2/IMD is kinetically selective for CLR-RAMP3, known as the AM₂R, and we define the structural basis for its distinct kinetics. In live cell biosensor assays, AM2/IMD-AM₂R elicited substantially longer duration cAMP signaling than the eight other peptide-receptor combinations. AM2/IMD and AM bound the AM₂R with similar equilibrium affinities, but AM2/IMD had a much slower off-rate and longer receptor residence time, thus explaining its prolonged signaling capacity. Peptide and receptor chimeras and mutagenesis were used to map the regions responsible for the distinct binding and signaling kinetics to the AM2/IMD mid-region and the RAMP3 extracellular domain (ECD). Molecular dynamics simulations revealed how the former forms stable interactions at the CLR ECD-transmembrane domain interface and how the latter augments the CLR ECD binding pocket to anchor the AM2/IMD C-terminus. These two strong binding components only combine in the AM₂R. Our findings uncover AM2/IMD-AM₂R as a cognate pair with unique temporal features, reveal how AM2/IMD and RAMP3 collaborate to shape CLR signaling, and have significant implications for AM2/IMD biology.

Breaking free from the crystal lattice: Structural biology in solution to study protein degraders

Structural biology offers a versatile arsenal of techniques and methods to investigate the structure and conformational dynamics of proteins and their assemblies. The growing field of targeted protein degradation centres on the premise of developing small molecules, termed degraders, to induce proximity between an E3 ligase and a protein of interest to be signalled for degradation. This new drug modality brings with it new opportunities and challenges to structural biologists. Here we discuss how several structural biology techniques, including nuclear magnetic resonance, cryo-electron microscopy, structural mass spectrometry and small angle scattering, have been explored to complement X-ray crystallography in studying degraders and their ternary complexes. Together the studies covered in this review make a case for the invaluable perspectives that integrative structural biology techniques in solution can bring to understanding ternary complexes and designing degraders.

Quality over quantity: Sampling high probability rare events with the weighted ensemble algorithm

The prediction of (un)binding rates and free energies is of great significance to the drug design process. Although many enhanced sampling algorithms and approaches have been developed, there is not yet a reliable workflow to predict these quantities. Previously we have shown that free energies and transition rates can be calculated by directly simulating the binding and unbinding processes with our variant of the WE algorithm "Resampling of Ensembles by Variation Optimization", or "REVO". Here, we calculate binding free energies retrospectively for three SAMPL6 host-guest systems and prospectively for a SAMPL9 system to test a modification of REVO that restricts its cloning behavior in quasi-unbound states. Specifically, trajectories cannot clone if they meet a physical requirement that represents a high likelihood of unbinding, which in the case of this work is a center-of-mass to center-of-mass distance. The overall effect of this change was difficult to predict, as it results in fewer unbinding events each of which with a much higher statistical weight. For all four systems tested, this new strategy produced either more accurate unbinding free energies or more consistent results between simulations than the standard REVO algorithm. This approach is highly flexible, and any feature of interest for a system can be used to determine cloning eligibility. These findings thus constitute an important improvement in the calculation of transition rates and binding free energies with the weighted ensemble method.

Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction

Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.

Functional E3 ligase hotspots and resistance mechanisms to small-molecule degraders

Targeted protein degradation is a novel pharmacology established by drugs that recruit target proteins to E3 ubiquitin ligases. Based on the structure of the degrader and the target, different E3 interfaces are critically involved, thus forming defined 'functional hotspots'. Understanding disruptive mutations in functional hotspots informs on the architecture of the assembly, and highlights residues susceptible to acquire resistance phenotypes. Here we employ haploid genetics to show that hotspot mutations cluster in substrate receptors of hijacked ligases, where mutation type and frequency correlate with gene essentiality. Intersection with deep mutational scanning revealed hotspots that are conserved or specific for chemically distinct degraders and targets. Biophysical and structural validation suggests that hotspot mutations frequently converge on altered ternary complex assembly. Moreover, we validated hotspots mutated in patients that relapse from degrader treatment. In sum, we present a fast and widely accessible methodology to characterize small-molecule degraders and associated resistance mechanisms.

Crystallization of VHL-based PROTAC-induced ternary complexes

X-ray crystal structures of PROTAC-induced ternary complexes provide invaluable insights into the critical species underpinning PROTAC mode of action, explain protein degradation selectivity profiles, and can guide rational degrader design. Nevertheless, crystallization of the ternary complexes formed by PROTACs remains an important bottleneck in employing this method. This is mainly due to the potential flexibility and heterogeneity that is inherent to a non-native protein-protein complex mediated by a small molecule, which together can hamper crystallization of the desired species. To overcome this limitation, selecting PROTAC compounds that enable the formation of stable, high-affinity and preferably cooperative ternary complexes in stoichiometric amount is, in our experience, critical to the success of co-crystallization studies. In this chapter, examples of stable PROTAC-mediated ternary complexes are illustrated. Learnings from biophysical & biochemical data are used as a guideline in achieving the highest "crystallizability" of ternary complexes. A case study of VHL-based SMARCA2 PROTAC degrader ternary complex crystallization is described. The procedure includes over-expression and purification of the E3 ligase and target protein, forming (and sometimes isolating) the ternary complex, and crystallizing it. The protocols can be applied for other combinations of E3 ligase, PROTAC and target protein.

Adrenomedullin 2/intermedin is a slow off-rate, long-acting endogenous agonist of the adrenomedullin 2 G protein-coupled receptor

The signaling peptides adrenomedullin 2/intermedin (AM2/IMD), adrenomedullin (AM), and CGRP have overlapping and distinct functions in the cardiovascular, lymphatic, and nervous systems by activating three shared receptors comprised of the class B GPCR CLR in complex with a RAMP1, -2, or -3 modulatory subunit. Here, we report that AM2/IMD, which is thought to be a non-selective agonist, is kinetically selective for CLR-RAMP3, known as the AM 2 R. AM2/IMD-AM 2 R elicited substantially longer duration cAMP signaling than the eight other peptide-receptor combinations due to AM2/IMD slow off-rate binding kinetics. The regions responsible for the slow off-rate were mapped to the AM2/IMD mid-region and the RAMP3 extracellular domain. MD simulations revealed how these bestow enhanced stability to the complex. Our results uncover AM2/IMD-AM 2 R as a cognate pair with unique temporal features, define the mechanism of kinetic selectivity, and explain how AM2/IMD and RAMP3 collaborate to shape the signaling output of a clinically important GPCR.

Developments in rapid hydrogen-deuterium exchange methods

Biological macromolecules, such as proteins, nucleic acids, and carbohydrates, contain heteroatom-bonded hydrogens that undergo exchange with solvent hydrogens on timescales ranging from microseconds to hours. In hydrogen-deuterium exchange mass spectrometry (HDX-MS), this exchange process is used to extract information about biomolecular structure and dynamics. This minireview focuses on millisecond timescale HDX-MS measurements, which, while less common than 'conventional' timescale (seconds to hours) HDX-MS, provide a unique window into weakly structured species, weak (or fast cycling) binding interactions, and subtle shifts in conformational dynamics. This includes intrinsically disordered proteins and regions (IDPs/IDRs) that are associated with cancer and amyloidotic neurodegenerative disease. For nucleic acids and carbohydrates, structures such as isomers, stems, and loops, can be elucidated and overall structural rigidity can be assessed. We will provide a brief overview of technical developments in rapid HDX followed by highlights of various applications, emphasising the importance of broadening the HDX timescale to improve throughput and to capture a wider range of function-relevant dynamic and structural shifts.

Investigation into the Use of Encorafenib to Develop Potential PROTACs Directed against BRAFV600E Protein

BRAF is a serine/threonine kinase frequently mutated in human cancers. BRAF^V600E mutated protein is targeted through the use of kinase inhibitors which are approved for the treatment of melanoma; however, their long-term efficacy is hampered by resistance mechanisms. The PROTAC-induced degradation of BRAF^V600E has been proposed as an alternative strategy to avoid the onset of resistance. In this study, we designed a series of compounds where the BRAF kinase inhibitor encorafenib was conjugated to pomalidomide through different linkers. The synthesized compounds maintained their ability to inhibit the kinase activity of mutated BRAF with IC₅₀ values in the 40-88 nM range. Selected compounds inhibited BRAF^V600E signaling and cellular proliferation of A375 and Colo205 tumor cell lines. Compounds 10 and 11, the most active of the series, were not able to induce degradation of mutated BRAF. Docking and molecular dynamic studies, conducted in comparison with the efficient BRAF degrader P5B, suggest that a different orientation of the linker bearing the pomalidomide substructure, together with a decreased mobility of the solvent-exposed part of the conjugates, could explain this behavior.

Modeling the Effect of Cooperativity in Ternary Complex Formation and Targeted Protein Degradation Mediated by Heterobifunctional Degraders

Chemically induced proximity between certain endogenous enzymes and a protein of interest (POI) inside cells may cause post-translational modifications to the POI with biological consequences and potential therapeutic effects. Heterobifunctional (HBF) molecules that bind with one functional part to a target POI and with the other to an E3 ligase induce the formation of a target-HBF-E3 ternary complex, which can lead to ubiquitination and proteasomal degradation of the POI. Targeted protein degradation (TPD) by HBFs offers a promising approach to modulate disease-associated proteins, especially those that are intractable using other therapeutic approaches, such as enzymatic inhibition. The three-way interactions among the HBF, the target POI, and the ligase-including the protein-protein interaction between the POI and the ligase-contribute to the stability of the ternary complex, manifested as positive or negative binding cooperativity in its formation. How such cooperativity affects HBF-mediated degradation is an open question. In this work, we develop a pharmacodynamic model that describes the kinetics of the key reactions in the TPD process, and we use this model to investigate the role of cooperativity in the ternary complex formation and in the target POI degradation. Our model establishes the quantitative connection between the ternary complex stability and the degradation efficiency through the former's effect on the rate of catalytic turnover. We also develop a statistical inference model for determining cooperativity in intracellular ternary complex formation from cellular assay data and demonstrate it by quantifying the change in cooperativity due to site-directed mutagenesis at the POI-ligase interface of the SMARCA2-ACBI1-VHL ternary complex. Our pharmacodynamic model provides a quantitative framework to dissect the complex HBF-mediated TPD process and may inform the rational design of effective HBF degraders.