cBinderDB: a covalent binding agent database

TopCysteineDB: A Cysteinome-wide Database Integrating Structural and Chemoproteomics Data for Cysteine Ligandability Prediction

Development of targeted covalent inhibitors and covalent ligand-first approaches have emerged as a powerful strategy in drug design, with cysteines being attractive targets due to their nucleophilicity and relative scarcity. While structural biology and chemoproteomics approaches have generated extensive data on cysteine ligandability, these complementary data types remain largely disconnected. Here, we present TopCysteineDB, a comprehensive resource integrating structural information from the PDB with chemoproteomics data from activity-based protein profiling experiments. Analysis of the complete PDB yielded 264,234 unique cysteines, while the proteomics dataset encompasses 41,898 detectable cysteines across the human proteome. Using TopCovPDB, an automated classification pipeline complemented by manual curation, we identified 787 covalent cysteines and systematically categorized other functional roles, including metal-binding, cofactor-binding, and disulfide bonds. Mapping residue-wise structural information to sequence space enabled cross-referencing between structural and proteomics data, creating a unified view of cysteine ligandability. For TopCySPAL, a machine learning model was developed, integrating structural features and proteomics data, achieving strong predictive performance (AUROC: 0.964, AUPRC: 0.914) and robust generalization to novel cases. TopCysteineDB and TopCySPAL are freely accessible through a webinterface, TopCysteineDBApp (https://topcysteinedb.hhu.de/), designed to facilitate exploration of cysteine sites across the human proteome. The interface provides an interactive visualization featuring a color-coded mapping of chemoproteomics data onto cysteine site structures and the highlighting of identified peptide sequences. It offers customizable dataset downloads and ligandability predictions for user-provided structures. This resource advances targeted covalent inhibitor design by providing integrated access to previously dispersed data types and enabling systematic analysis and prediction of cysteine ligandability.

Computational Methods for Predicting Chemical Reactivity of Covalent Compounds

In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities. Accurately predicting and modulating this reactivity have become a critical focus in the field. In this work, we compiled a data set of 419 cysteine-targeted covalent compounds and their reactivity through an extensive literature review. Employing machine learning, deep learning, and quantum mechanical calculations, we evaluated the intrinsic reactivity of the covalent compounds. Our FP-Stack models demonstrated robust Pearson and Spearman correlations of approximately 0.80 and 0.75 on the test set, respectively. This empowers rapid and accurate reactivity predictions, significantly reducing computational costs and streamlining structural handling and experimental procedures. Experimental validation on acrylamide compounds underscored the predictive efficacy of our model. This study presents an efficient computational tool for the reactivity prediction of covalent compounds and is expected to offer valuable insights for guiding covalent drug discovery and development.

Development of ketalized unsaturated saccharides as multifunctional cysteine-targeting covalent warheads

Multi-functional cysteine-targeting covalent warheads possess significant therapeutic potential in medicinal chemistry and chemical biology. Herein, we present novel unsaturated and asymmetric ketone (oxazolinosene) scaffolds that selectively conjugate cysteine residues of peptides and bovine serum albumin under normal physiological conditions. This unsaturated saccharide depletes GSH in NCI-H1299 cells, leading to anti-tumor effects in vitro. The acetyl group of the ketal moiety on the saccharide ring can be converted to other carboxylic acids in a one-pot synthesis. In this way, the loaded acid can be click-released during cysteine conjugation, making the oxazolinosene a potential multifunctional therapeutic agent. The reaction kinetic model for oxazolinosene conjugation to GSH is well established and was used to evaluate oxazolinosene reactivity. The aforementioned oxazolinosenes were stereoselectively synthesized via a one-step reaction of nitriles with saccharides and conveniently converted into a series of α, β-unsaturated ketone N-glycosides as prevalent synthetic building blocks. The reaction mechanisms of oxazolinosene synthesis were investigated through calculations and validated with control experiments. Overall, these oxazolinosenes can be easily synthesized and developed as cysteine-targeted covalent warheads carrying useful click-releasing groups.

CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

Discovery of a Tunable Heterocyclic Electrophile 4-Chloro-pyrazolopyridine That Defines a Unique Subset of Ligandable Cysteines

Electrophilic small molecules with novel reactivity are powerful tools that enable activity-based protein profiling and covalent inhibitor discovery. Here, we report a reactive heterocyclic scaffold, 4-chloro-pyrazolopyridine (CPzP) for selective modification of proteins via a nucleophilic aromatic substitution (SNAr) mechanism. Chemoproteomic profiling reveals that CPzPs engage cysteines within functionally diverse protein sites including ribosomal protein S5 (RPS5), inosine monophosphate dehydrogenase 2 (IMPDH2), and heat shock protein 60 (HSP60). Through the optimization of appended recognition elements, we demonstrate the utility of CPzP for covalent inhibition of prolyl endopeptidase (PREP) by targeting a noncatalytic active-site cysteine. This study suggests that the proteome reactivity of CPzPs can be modulated by both electronic and steric features of the ring system, providing a new tunable electrophile for applications in chemoproteomics and covalent inhibitor design.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Two-Step Covalent Docking with Attracting Cavities

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

Covalent Inhibition by a Natural Product-Inspired Latent Electrophile

Strategies to target specific protein cysteines are critical to covalent probe and drug discovery. 3-Bromo-4,5-dihydroisoxazole (BDHI) is a natural product-inspired, synthetically accessible electrophilic moiety that has previously been shown to react with nucleophilic cysteines in the active site of purified enzymes. Here, we define the global cysteine reactivity and selectivity of a set of BDHI-functionalized chemical fragments using competitive chemoproteomic profiling methods. Our study demonstrates that BDHIs capably engage reactive cysteine residues in the human proteome and the selectivity landscape of cysteines liganded by BDHI is distinct from that of haloacetamide electrophiles. Given its tempered reactivity, BDHIs showed restricted, selective engagement with proteins driven by interactions between a tunable binding element and the complementary protein sites. We validate that BDHI forms covalent conjugates with glutathione S-transferase Pi (GSTP1) and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1), emerging anticancer targets. BDHI electrophile was further exploited in Bruton's tyrosine kinase (BTK) inhibitor design using a single-step late-stage installation of the warhead onto acrylamide-containing compounds. Together, this study expands the spectrum of optimizable chemical tools for covalent ligand discovery and highlights the utility of 3-bromo-4,5-dihydroisoxazole as a cysteine-reactive electrophile.

Rational Design of Covalent Kinase Inhibitors by an Integrated Computational Workflow (Kin-Cov)

Covalent kinase inhibitors (CKIs) hold great promise for drug development. However, examples of computationally guided design of CKIs are still scarce. Here, we present an integrated computational workflow (Kin-Cov) for rational design of CKIs. The design of the first covalent leucine-zipper and sterile-α motif kinase (ZAK) inhibitor was presented as an example to showcase the power of computational workflow for CKI design. The two representative compounds, 7 and 8, inhibited ZAK kinase with half-maximal inhibitory concentration (IC₅₀) values of 9.1 and 11.5 nM, respectively. Compound 8 displayed an excellent ZAK target specificity in Kinome profiling against 378 wild-type kinases. Structural biology and cell-based Western blot washout assays validated the irreversible binding characteristics of the compounds. Our study presents a rational approach for the design of CKIs based on the reactivity and accessibility of nucleophilic amino acid residues in a kinase. The workflow is generalizable and can be applied to facilitate CKI-based drug design.

CovBinderInPDB: A Structure-Based Covalent Binder Database

Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned. Then, residue-binder adducts, which are products of chemical reactions between targeted residues and covalent binders, were recovered with the help of the Chemical Component Dictionary in PDB. Finally, several strategies were employed to curate the pre-reaction forms of covalent binders from the adducts. Our curated CovBinderInPDB database contains 7375 covalent modifications in which 2189 unique covalent binders target nine types of amino acid residues (Cys, Lys, Ser, Asp, Glu, His, Met, Thr, and Tyr) from 3555 complex structures of 1170 unique protein chains. This database would set a solid foundation for developing and benchmarking computational strategies for covalent modulator design and is freely accessible at https://yzhang.hpc.nyu.edu/CovBinderInPDB.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein–ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

Systematic Studies on the Protocol and Criteria for Selecting a Covalent Docking Tool

With the resurgence of drugs with covalent binding mechanisms, much attention has been paid to docking methods for the discovery of targeted covalent inhibitors. The existence of many available covalent docking tools has inspired development of a systematic and objective procedure and criteria with which to evaluate these programs. In order to find a tool appropriate to studies of a covalently binding system, protocols and criteria are proposed for protein–ligand covalent docking studies. This paper consists of three sections: (1) curating a standard data set to evaluate covalent docking tools objectively; (2) establishing criteria to measure the performance of a tool applied for docking ligands into a complex system; and (3) creating a protocol to evaluate and select covalent binding tools. The protocols were applied to evaluate four covalent docking tools (MOE, GOLD, CovDock, and ICM-Pro) and parameters affecting covalent docking performance were investigated.