Identification of the first structurally validated covalent ligands of the small GTPase RAB27A

A novel Rab27A construct enables elucidation of covalent ligand binding, paving the way for structure-guided approaches against this challenging target.

KRAS is vulnerable to reversible switch-II pocket engagement in cells

Current small molecule inhibitors of KRAS (G12C) bind irreversibly in the switch-II pocket, exploiting the strong nucleophilicity of the acquired cysteine as well as the preponderance of the GDP-bound form of this mutant. Nevertheless, many oncogenic KRAS mutants lack these two features, and it remains unknown whether targeting the switch-II pocket is a practical therapeutic approach for KRAS mutants beyond G12C. Here we use NMR spectroscopy and a novel cellular KRAS engagement assay to address this question by examining a collection of SII-P ligands from the literature and from our own laboratory. We show that the switch-II pockets of many GTP hydrolysis-deficient KRAS hotspot (G12, G13, Q61) mutants are accessible using non-covalent ligands, and that this accessibility is not necessarily coupled to the GDP state of KRAS. The results we describe here emphasize the switch-II pocket as a privileged drug binding site on KRAS and unveil new therapeutic opportunities in RAS-driven cancer.

Identification of pyrogallol as a warhead in design of covalent inhibitors for the SARS-CoV-2 3CL protease

The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) urgently needs an effective cure. 3CL protease (3CLpro) is a highly conserved cysteine proteinase that is indispensable for coronavirus replication, providing an attractive target for developing broad-spectrum antiviral drugs. Here we describe the discovery of myricetin, a flavonoid found in many food sources, as a non-peptidomimetic and covalent inhibitor of the SARS-CoV-2 3CLpro. Crystal structures of the protease bound with myricetin and its derivatives unexpectedly revealed that the pyrogallol group worked as an electrophile to covalently modify the catalytic cysteine. Kinetic and selectivity characterization together with theoretical calculations comprehensively illustrated the covalent binding mechanism of myricetin with the protease and demonstrated that the pyrogallol can serve as an electrophile warhead. Structure-based optimization of myricetin led to the discovery of derivatives with good antiviral activity and the potential of oral administration. These results provide detailed mechanistic insights into the covalent mode of action by pyrogallol-containing natural products and a template for design of non-peptidomimetic covalent inhibitors against 3CLpros, highlighting the potential of pyrogallol as an alternative warhead in design of targeted covalent ligands.

Covalent Docking in Drug Discovery: Scope and Limitations

Drug discovery efforts for new covalent inhibitors have drastically increased in the last few years. The binding mechanism of covalent compounds entails the formation of a chemical bond between their electrophilic warhead group and the protein of interest. The use of moderately reactive warheads targeting nonconserved nucleophilic residues can improve the affinity and selectivity profiles of covalent binders as compared to their non-covalent analogs. Recent advances have also enabled their use as chemical probes to disclose novel and also less tractable targets. Increasing interest in covalent drug discovery prompted the development of new computational tools, including covalent docking methods, that are available to predict the binding mode and affinity of covalent ligands. These tools integrate conventional non-covalent docking and scoring schemes by modeling the newly formed covalent bond and the interactions occurring at the reaction site. In this review, we provide a thorough analysis of state-of-the-art covalent docking programs by highlighting their main features and current limitations. Focusing on the implemented algorithms, we show the differences in handling the formation of the new covalent bond and their relative impact on the prediction. This analysis provides a comprehensive overview of the current technology and suggests future improvements in computer-aided covalent drug design. Finally, discussing successful retrospective and prospective covalent docking-based virtual screening applications, we intend to identify best practices for the drug discovery community.

Where Electrophile Signaling and Covalent Ligand–Target Mining Converge

Interests in learning how to engineer most effective covalent ligands, identify novel functional targets, and define precise mechanism-of-action are rapidly growing in both academia and pharmaceutical industries. We here illuminate the establishment of a multifunctional platform that offers new capabilities to logically engineer covalent ligands and dissect 'on-target' bioactivity with precise biological context and precision hitherto inaccessible. Broadly aimed at non-specialist readers, this opinion piece is aimed to stoke the interest of emerging chemists and biologists/bioengineers, but the underlying technological and conceptual topicality is anticipated to also appeal to experts leading ligand–target mining, validation, and -discovery research programs.

Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

<div> <div> <div> <div> <p>One of the most important SARS-CoV-2 protein targets for therapeutics is the 3C-like protease (main protease, Mpro). In our previous work1​we used the first Mpro crystal structure to become available, 6LU7. On February 4, 2020 Insilico Medicine released the first potential novel protease inhibitors designed using a ​de novo,​AI-driven generative chemistry approach. Nearly 100 X-ray structures of Mpro co-crystallized both with covalent and non-covalent ligands have been published since then. Here we utilize the recently published 6W63 crystal structure of Mpro complexed with a non-covalent inhibitor and combined two approaches used in our previous study: ligand-based and crystal structure-based. We published 10 representative structures for potential development with 3D representation in PDB format and welcome medicinal chemists for broad discussion and generated output analysis. The molecules in SDF format and PDB-models for generated protein-ligand complexes are available here and at https://insilico.com/ncov-sprint/.​Medicinal chemistry VR analysis was provided by ​Nanome team and the video of VR session is available at ​https://bit.ly/ncov-vr.​ </p> </div> </div> </div> </div>

Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

<div> <div> <div> <div> <p>One of the most important SARS-CoV-2 protein targets for therapeutics is the 3C-like protease (main protease, Mpro). In our previous work1​we used the first Mpro crystal structure to become available, 6LU7. On February 4, 2020 Insilico Medicine released the first potential novel protease inhibitors designed using a ​de novo,​AI-driven generative chemistry approach. Nearly 100 X-ray structures of Mpro co-crystallized both with covalent and non-covalent ligands have been published since then. Here we utilize the recently published 6W63 crystal structure of Mpro complexed with a non-covalent inhibitor and combined two approaches used in our previous study: ligand-based and crystal structure-based. We published 10 representative structures for potential development with 3D representation in PDB format and welcome medicinal chemists for broad discussion and generated output analysis. The molecules in SDF format and PDB-models for generated protein-ligand complexes are available here and at https://insilico.com/ncov-sprint/.​Medicinal chemistry VR analysis was provided by ​Nanome team and the video of VR session is available at ​https://bit.ly/ncov-vr.​ </p> </div> </div> </div> </div>