Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries.

Covalent Stapling of the Cereblon Sensor Loop Histidine Using Sulfur-Heterocycle Exchange

Site-specific modification of amino acid residues in protein binding pockets using sulfonyl exchange chemistry expands the druggable proteome by enabling the development of covalent modulators that target residues beyond cysteine. Sulfonyl fluoride and triazole electrophiles were incorporated previously into the cereblon (CRBN) molecular glue degrader EM12, to covalently engage His353 within the CRBN sensor loop, but these probes had poor human plasma stability. Attenuation of intrinsic reactivity through the development of sulfonyl pyrazoles, imidazoles, and nucleobases enhanced plasma stability, and several compounds retained efficient labeling of His353. For example, sulfonyl imidazole EM12-SO2Im covalently blocked the CRBN binding site and possessed excellent metabolic stability in human plasma, liver microsomes, and hepatocytes. These results highlight the potential suitability of sulfonyl imidazole and related sulfur(VI)-diazole exchange (SuDEx) warheads for covalent drug development and further exemplify the therapeutic promise of site-specific histidine targeting.

Proteome-wide structural analysis identifies warhead- and coverage-specific biases in cysteine-focused chemoproteomics

Covalent drug discovery has undergone a resurgence over the past two decades and reactive cysteine profiling has emerged in parallel as a platform for ligand discovery through on- and off-target profiling; however, the scope of this approach has not been fully explored at the whole-proteome level. We combined AlphaFold2-predicted side-chain accessibilities for >95% of the human proteome with a meta-analysis of eighteen public cysteine profiling datasets, totaling 44,187 unique cysteine residues, revealing accessibility biases in sampled cysteines primarily dictated by warhead chemistry. Analysis of >3.5 million cysteine-fragment interactions further showed that hit elaboration and optimization drives increased bias against buried cysteine residues. Based on these data, we suggest that current profiling approaches cover a small proportion of potential ligandable cysteine residues and propose future directions for increasing coverage, focusing on high-priority residues and depth. All analysis and produced resources are freely available and extendable to other reactive amino acids.