Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation

Abstract

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug−target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug−target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group−protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

“…This approach allows for assessment of GFE scores of individual classified atoms within the ligand that may be summed yielding the LGFE score that represents the ligand binding affinity. 3,5,50,57 been shown that ligand ranking using the SILCS methodology is comparable with the FEP approaches 51 while offering an improvement in computational speed of over 2 orders of magnitude. In addition, SILCS-MC has been used in conjunction with FragMaps for lipid bilayers to calculate the free energy profiles of ligands across the bilayers, information subsequently used in conjunction with a deep neural net to develop an artificial intelligence tool for prediction of ligand passive permeability.…”

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

“…A comprehensive study of several popular docking tools by Keserű et al observed a protein-dependent performance of docking approaches and thus recommends choosing an optimal docking tool based on the target protein. To avoid these uncertainties and to increase accuracy, it is often recommended to use a combination of tools and/or develop a robust workflow − to achieve best results. To summarize, covalent docking is useful in ranking different inhibitors with the same warhead and/or to generate an initial binding pose that incorporates both the orientation of the covalent bond as well as the optimal non-covalent interactions.…”

“…Detailed workflow of the SILCS-covalent drug design process. Reproduced with permission from ref . Copyright 2023 American Chemical Society.…”

“…MacKerell et al 74 undertook research to look into the possibilities of the SILCS (Site Identification by Ligand Competitive Saturation) method in covalent drug development. It was motivated by its high computational efficiency and wide application in drug discovery in general.…”

“…These tools are widely used by researchers both in academia and in industry with MOE and CovDock being the champion covalent docking tools from Chemical Computing Group Inc. and Schrodinger Inc., respectively. Most of these tools primarily rely on atom−atom distance (e.g., MacKerell et al 74 ) and/or predefined reaction types (e.g., CovDock by Schrodinger, reaction-based covalent docking in MOE by CCG) to make the covalent bond between the electrophilic warhead on an inhibitor the and nucleophilic atom on the reactive amino acid residue. While the performance of these tools turns out to be good to excellent in many cases, they often fail miserably to distinguish between binders vs nonbinders, making them system-dependent.…”