Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Dissecting oxidative folding of conotoxins using 3D structures of cysteine mutants predicted by AlphaFold 3: A case study of α-conotoxin RgIA, χ-conotoxin CMrVIA and ω-conotoxin MVIIA-Gly

The ability of AlphaFold 3 to accurately predict the 3D structure of polypeptides has been explored to investigate the oxidative folding steps of conotoxins. The peptides α-conotoxin RgIA (α-RgIA) and χ-conotoxin CMrVIA (χ-CMrVIA) share a similar cysteine pattern but differ in their native disulfide connectivity. These short peptides, containing two intramolecular disulfides, may undergo sequential steps of disulfide formation during the oxidative folding process. The current report computed all six possible single disulfide alanine mutants of the peptides and predicted their 3D structures using the AlphaFold 3 server. The potential energy of the conformers derived from the five predicted model structures of the peptides was calculated using the OPLS4 force field in Schrödinger's MacroModel software. The relative potential energy of the single disulfide mutant peptides was computed using the Boltzmann-weighted average energy of the conformers of the corresponding peptides. [C2A,C8A]α-RgIA and [C2A,C11A]χ-CMrVIA are the most stable forms, corresponding to the native single disulfide intermediate analogues. Accordingly, the folding events for α-RgIA are C3-C12 followed by C2-C8, while for χ-CMrVIA, they are C3-C8 followed by C2-C11 connectivity. The current report also explored the native folding steps of an Inhibitory Cystine Knot (ICK) motif peptide ω-conotoxin-MVIIA-Gly using one/two cysteine disulfide alanine mutants. The computation of relative potential energy of the mutant peptides indicates the formation of C15-C25 followed by C8-C20 and C1-C16 disulfide bonds. The newly proposed technique that combines AlphaFold 3 with MacroModel conformational sampling tool is allowing to identify the oxidative folding steps of disulfide-rich peptides.

Targeted inhibition of WIP1 and histone H3K27 demethylase activity synergistically suppresses neuroblastoma growth

High-risk neuroblastoma frequently exhibits segmental gain of chromosome 17q, including the locus of PPM1D, which encodes the phosphatase WIP1, a regulator of p53 activity, DNA repair, and apoptosis. High expression of PPM1D is correlated to poor prognosis, and genetic or pharmacologic inhibition of WIP1 suppresses neuroblastoma growth. Here, we show that combining drugs that target WIP1 and H3K27 demethylation induces synergistic cytotoxicity in neuroblastoma. We screened 527 different compounds together with inhibitors of WIP1 and identified a strong cytotoxic synergism between the WIP1 inhibitor SL-176 and GSK-J4, a specific inhibitor of the H3K27 demethylase JMJD3. Viability assays in neuroblastoma cell lines and treatment of tumor spheroids confirmed the synergistic effect of combining SL-176 with GSK-J4. Immunoblot experiments demonstrated a marked effect on WIP1 downstream targets and apoptosis markers, while qPCR showed a synergistic upregulation of p53 downstream targets PUMA and p21. RNA sequencing revealed a vast number of differentially expressed genes, suggesting a pervasive effect of this drug combination on transcription, with enrichment of pathways involved in DNA damage response. Finally, this drug combination was confirmed to reduce tumor growth in zebrafish xenograft experiments. In conclusion, the combination of the WIP1 inhibitor SL-176 and the epigenetic modifier GSK-J4 induces synergistic cytotoxicity in neuroblastoma cells by potentiating p53 downstream effects.

AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles

Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction to the masses and opening up innumerable new avenues of research. Despite this enormous success, calling structure prediction, much less protein folding and related problems, "solved" is dangerous, as doing so could stymie further progress. Imagine what the world would be like if we had declared flight solved after the first commercial airlines opened and stopped investing in further research and development. Likewise, there are still important limitations to structure prediction that we would benefit from addressing. Moreover, we are limited in our understanding of the enormous diversity of different structures a single protein can adopt (called a conformational ensemble) and the dynamics by which a protein explores this space. What is clear is that conformational ensembles are critical to protein function, and understanding this aspect of protein dynamics will advance our ability to design new proteins and drugs.

Crystal structure and mechanistic studies of the PPM1D serine/threonine phosphatase catalytic domain

Protein phosphatase 1D (PPM1D, Wip1) is induced by the tumor suppressor p53 during DNA damage response signaling and acts as an oncoprotein in several human cancers. Although PPM1D is a potential therapeutic target, insights into its atomic structure were challenging due to flexible regions unique to this family member. Here, we report the first crystal structure of the PPM1D catalytic domain to 1.8 Å resolution. The structure reveals the active site with two Mg2+ ions bound, similar to other structures. The flap subdomain and B-loop, which are crucial for substrate recognition and catalysis, were also resolved, with the flap forming two short helices and three short β-strands that are followed by an irregular loop. Unexpectedly, a nitrogen-oxygen-sulfur bridge was identified in the catalytic domain. Molecular dynamics simulations and kinetic studies provided further mechanistic insights into the regulation of PPM1D catalytic activity. In particular, the kinetic experiments demonstrated a magnesium concentration-dependent lag in PPM1D attaining steady-state velocity, a feature of hysteretic enzymes that show slow transitions compared with catalytic turnover. All combined, these results advance the understanding of PPM1D function and will support the development of PPM1D-targeted therapeutics.

Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling

Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Exploring Conformational Landscapes and Cryptic Binding Pockets in Distinct Functional States of the SARS-CoV-2 Omicron BA.1 and BA.2 Trimers: Mutation-Induced Modulation of Protein Dynamics and Network-Guided Prediction of Variant-Specific Allosteric Binding Sites

A significant body of experimental structures of SARS-CoV-2 spike trimers for the BA.1 and BA.2 variants revealed a considerable plasticity of the spike protein and the emergence of druggable binding pockets. Understanding the interplay of conformational dynamics changes induced by the Omicron variants and the identification of cryptic dynamic binding pockets in the S protein is of paramount importance as exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In the current study, we explore conformational landscapes and characterize the universe of binding pockets in multiple open and closed functional spike states of the BA.1 and BA.2 Omicron variants. By using a combination of atomistic simulations, a dynamics network analysis, and an allostery-guided network screening of binding pockets in the conformational ensembles of the BA.1 and BA.2 spike conformations, we identified all experimentally known allosteric sites and discovered significant variant-specific differences in the distribution of binding sites in the BA.1 and BA.2 trimers. This study provided a structural characterization of the predicted cryptic pockets and captured the experimentally known allosteric sites, revealing the critical role of conformational plasticity in modulating the distribution and cross-talk between functional binding sites. We found that mutational and dynamic changes in the BA.1 variant can induce the remodeling and stabilization of a known druggable pocket in the N-terminal domain, while this pocket is drastically altered and may no longer be available for ligand binding in the BA.2 variant. Our results predicted the experimentally known allosteric site in the receptor-binding domain that remains stable and ranks as the most favorable site in the conformational ensembles of the BA.2 variant but could become fragmented and less probable in BA.1 conformations. We also uncovered several cryptic pockets formed at the inter-domain and inter-protomer interface, including functional regions of the S2 subunit and stem helix region, which are consistent with the known role of pocket residues in modulating conformational transitions and antibody recognition. The results of this study are particularly significant for understanding the dynamic and network features of the universe of available binding pockets in spike proteins, as well as the effects of the Omicron-variant-specific modulation of preferential druggable pockets. The exploration of predicted druggable sites can present a new and previously underappreciated opportunity for therapeutic interventions for Omicron variants through the conformation-selective and variant-specific targeting of functional sites involved in allosteric changes.