Underestimated Noncovalent Interactions in Protein Data Bank

Identification of potential inhibitors against Staphylococcus aureus shikimate dehydrogenase through virtual screening and susceptibility test

Staphylococcus aureus shikimate dehydrogenase (SaSDH) plays a crucial role in the growth of Staphylococcus aureus (S. aureus), but absent in mammals and therefore a potential target for antibacterial drugs to treat drug-resistant S. aureus infection. In this study, a 3D model of SaSDH was constructed by homology modelling and inhibitors of SaSDH were screened through virtual screening. (-)-Gallocatechin gallate and rhodiosin were identified as inhibitors with Kis of 2.47 μM and 73.38 μM, respectively. Molecular docking and isothermal titration calorimetry showed that both inhibitors interact with SaSDH with a KD of 44.65 μM for (-)-gallocatechin gallate and 16.45 μM for rhodiosin. Both inhibitors had antibacterial activity, showing MICs of 50 μg/mL for (-)-gallocatechin gallate and 250 μg/mL for rhodiosin against S. aureus. The current findings have the potential for identification of drugs to treat S. aureus infections by targeting SaSDH.

Structural, computational and in silico studies of 4-bromo-3-flurobenzonitrile as anti-Alzheimer and anti-Parkinson agents

A novel dimer of the 4-bromo-3-fluorobenzonitrile was crystallized and studied using a spectroscopic method such as the scanning electron microscope method. The computational simulations substantiated the structural analysis findings. The Hirshfeld surface analysis has been performed for visualizing, exploring and quantifying the intra and inter-molecular interactions that stabilize the crystal packing of the compound. The NBO and QTAIM analyses were applied to study the nature and origin of the attractive forces involved in the crystal structure. Further, the pharmacokinetic properties of the compound were evaluated, indicating good brain-blood barrier and central nervous system penetration capability. Hence, in silico studies was carried out to explore the binding pattern of the titled compound against acetylcholinesterase and butyrylcholinesterase, and tumor necrosis factor-alpha converting enzyme proteins using molecular docking and molecular dynamics simulations approach. Further, the titled compound is compared with standard drugs through molecular docking studies. The in silico studies finally predicts that the compound under investigation may act as a good inhibitor for treating Alzheimer's disease and further in vitro and in vivo studies may provide its therapeutic potential.Communicated by Ramaswamy H. Sarma.

Impact of Halogen Bonds on Protein-Peptide Binding and Protein Structural Stability Revealed by Computational Approaches

Halogen bonds (XBs) are essential noncovalent interactions in molecular recognition and drug design. Current studies on XBs in drug design mainly focus on the interactions between halogenated ligands and target proteins, lacking a systematic study of naturally existing and artificially prepared halogenated residue XBs (hr_XBs) and their characteristics. Here, we conducted a computational study on the potential hr_XBs in proteins/peptides using database searching, quantum mechanics calculations, and molecular dynamics simulations. XBs at the protein-peptide interaction interfaces are found to enhance their binding affinity. Additionally, the formation of intramolecular XBs (intra_XBs) within proteins may significantly contribute to the structural stability of structurally flexible proteins while having a minor impact on proteins with inherently high structural rigidity. Impressively, introducing halogens without the formation of intra_XBs may lead to a decrease in the protein structural stability. This study enriches our understanding of the roles and effects of halogenated residue XBs in biological systems.

Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.

Analyzing the aromatic-aromatic interactions in proteins: A2ID 2.0

The Aromatic-Aromatic Interactions Database (A2ID) is a comprehensive repository dedicated to documenting aromatic-aromatic (π-π) networks observed in experimentally determined protein structures. The first version of A2ID was reported in 2011 [Int J Biol Macromol, 2011, 48, 540]. It has undergone a series of significant updates, leading to its current version, which focuses on the identification and analysis of 3,444,619 π-π networks from proteins. The geometrical parameters such as centroid-centroid distances (r) and interplanar angles (ϕ) were used to identify and characterize π-π networks. It was observed that among the 84,500 proteins with at least one aromatic π-π network, about 92.50 % of the instances are found to be either 2π (77.34 %) or 3π (15.23 %) networks. The analysis of interacting amino acid pairs in 2π networks indicated a dominance of PHE residues followed by TYR. The updated version of A2ID incorporates analysis of π-π networks based on SCOP2 and ECOD classifiers, in addition to the existing SCOP, CATH, and EC classifications. This expanded scope allows researchers to explore the characteristics and functional implications of π-π networks in protein structures from multiple perspectives. The current version of A2ID along with its extensive dataset and detailed geometric information is publicly accessible using https://acds.neist.res.in/a2idv2.

AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics

Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.

Preferred microenvironments of halogen bonds and hydrogen bonds revealed using statistics and QM/MM calculation studies

Hydrogen bonds (HBs) and halogen bonds (XBs) are two essential non-covalent interactions for molecular recognition and drug design. As proteins are heterogeneous in structure, the microenvironments of protein structures should have effects on the formation of HBs and XBs with ligands. However, there are no systematic studies reported on this effect to date. For quantitatively describing protein microenvironments, we defined the local hydrophobicities (LHs) and local dielectric constants (LDCs) in this study. With the defined parameters, we conducted an elaborate database survey on the basis of 22 011 ligand-protein structures to explore the microenvironmental preference of HBs (91 966 in total) and XBs (1436 in total). The statistics show that XBs prefer hydrophobic microenvironments compared to HBs. The polar residues like ASP are more likely to form HBs with ligands, while nonpolar residues such as PHE and MET prefer XBs. Both the LHs and LDCs (10.69 ± 4.36 for HBs; 8.86 ± 4.00 for XBs) demonstrate that XBs are prone to hydrophobic microenvironments compared with HBs with significant differences (p < 0.001), indicating that evaluating their strengths in the corresponding environments should be necessary. Quantum Mechanics-Molecular Mechanics (QM/MM) calculations reveal that in comparison with vacuum environments, the interaction energies of HBs and XBs are decreased to varying degrees given different microenvironments. In addition, the strengths of HBs are impaired more than those of XBs when the local dielectric constant's difference between the XB microenvironments and the HB microenvironments is large.

Biomolecular 1D Necklace-like Nanostructures Tailoring 2D Janus Interfaces for Controllable 3D Enteric Biomaterials

Construction of well-ordered two-dimensional (2D) and three-dimensional (3D) assemblies using one-dimensional (1D) units is a hallmark of many biointerfaces such as skin. Mimicking the art of difunctional properties of biointerfaces, which skin exhibits as defense and shelter materials, has inspired the development of smart and responsive biomimetic interfaces. However, programming the long-range ordering of 1D base materials toward vigorous control over 2D and 3D hierarchical structures and material properties remains a daunting challenge. In this study, we put forward construction of 3D enteric biomaterials with a two-strata 2D Janus interface assembled from self-adaptation of 1D protein-polysaccharide nanostructures at an oil-water interface. The biomaterials feature a protein dermis accommodating oil droplets as a reservoir for bioactive compounds and a polysaccharide epidermis protecting them from gastric degradation. Furthermore, the epidermis can be fine-tuned with different thicknesses rendering enteric delivery of a bioactive cargo (coumarin-6) with controllable retention in the intestinal tract from 6 to 24 h. The results highlight a skin-inspired construction of enteric biomaterials by self-adaptation of 1D nanostructures at the oil-water interface toward 2D Janus biointerfaces and 3D microdevices, which can be tailored for intestinal treatments with intentional therapeutic efficacies.

The cation-π interaction in cysteine-rich domain of Smoothened is critical for its cholesterylation and function

The Hedgehog (Hh) signaling pathway is critical for embryonic development and tissue renewal. The G protein-coupled receptor (GPCR)-like protein Smoothened (SMO) is the central signal transducer in the Hh pathway. Cholesterol binds and then covalently links to the D95 residue of cysteine-rich domain (CRD) of human SMO. The cholesterylation of CRD is critical for SMO activation. SMO cholesterylation is a Ca 2+-boosted autoreaction that requires the formation of an ester bond between the side chains of D95 and Y130 as an intermediate. It is unknown whether other residues of SMO are involved in the esterification between D95 and cholesterol. In this study, we find that the SMO-CRD(27-192) can undergo cholesterylation. In addition to D95 and Y130, the residues critical for cholesterol modification include Y85, T88, T90, W109, W119, K133, E160 and F166. T88, W109, W119 and F166 also seem to be involved in protein folding. Notably, we find that Y85 and K133 form a cation-π interaction whose disruption abolishes cholesterylation and ciliary localization of SMO. This study highlights the mechanism and function of cholesterol modification of SMO.

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

Comprehensive Basis-Set Testing of Extended Symmetry-Adapted Perturbation Theory and Assessment of Mixed-Basis Combinations to Reduce Cost

Hybrid or "extended" symmetry-adapted perturbation theory (XSAPT) replaces traditional SAPT's treatment of dispersion with better performing alternatives while at the same time extending two-body (dimer) SAPT to a many-body treatment of polarization using a self-consistent charge embedding procedure. The present work presents a systematic study of how XSAPT interaction energies and energy components converge with respect to the choice of Gaussian basis set. Errors can be reduced in a systematic way using correlation-consistent basis sets, with aug-cc-pVTZ results converged within <0.1 kcal/mol. Similar (if slightly less systematic) behavior is obtained using Karlsruhe basis sets at much lower cost, and we introduce new versions with limited augmentation that are even more efficient. Pople-style basis sets, which are more efficient still, often afford good results if a large number of polarization functions are included. The dispersion models used in XSAPT afford much faster basis-set convergence as compared to the perturbative description of dispersion in conventional SAPT, meaning that "compromise" basis sets (such as jun-cc-pVDZ) are no longer required and benchmark-quality results can be obtained using triple-ζ basis sets. The use of diffuse functions proves to be essential, especially for the description of hydrogen bonds. The "δ(Hartree-Fock)" correction for high-order induction can be performed in double-ζ basis sets without significant loss of accuracy, leading to a mixed-basis approach that offers 4× speedup over the existing (cubic scaling) XSAPT approach.

Halogen Bonds Exist between Noncovalent Ligands and Natural Nucleic Acids

Because of their strong electron-rich properties, nucleic acids (NAs) can theoretically serve as halogen bond (XB) acceptors. From a PDB database survey, Kolář found that no XBs are formed between noncovalent ligands and NAs. Through statistical database analysis, quantum-mechanics/molecular-mechanics (QM/MM) optimizations, and energy calculations, we find that XBs formed between natural NAs and noncovalent ligands are primarily underestimated and that NAs can serve as XB acceptors to interact with noncovalent halogen ligands. Finally, through energy calculations, natural bond orbital analysis, and noncovalent interaction analysis, XBs are confirmed in 13 systems, among which two systems (445D and 4Q9Q) have relatively strong XBs. In addition, on the basis of energy scanning of four model systems, we explore the geometric rule for XB formation in NAs. This work will inspire researchers to utilize XBs in rational drug design targeting NAs.

Predicting and Understanding Non-Covalent Interactions Using Novel Forms of Symmetry-Adapted Perturbation Theory

Although sometimes derided as "weak" interactions, non-covalent forces play a critical role in ligand binding and crystal packing and in determining the conformational landscape of flexible molecules. Symmetry-adapted perturbation theory (SAPT) provides a framework for accurate ab initio calculation of intermolecular interactions and furnishes a natural decomposition of the interaction energy into physically meaningful components: semiclassical electrostatics (rigorously obtained from monomer charge densities), Pauli or steric repulsion, induction (including both polarization and charge transfer), and dispersion. This decomposition helps to foster deeper understanding of non-covalent interactions and can be used to construct transferable, physics-based force fields. Separability of the SAPT interaction energy also provides the flexibility to construct composite methods, a feature that we exploit to improve the description of dispersion interactions. These are challenging to describe accurately because they arise from nonlocal electron correlation effects that appear for the first time at second order in perturbation theory but are not quantitatively described at that level.As with all quantum-chemical methods, a major limitation of SAPT is nonlinear scaling of the computational cost with respect to system size. This cost can be significantly mitigated using "SAPT0(KS)", which incorporates monomer electron correlation by means of Kohn-Sham (KS) molecular orbitals from density functional theory (DFT), as well as by an "extended" theory called XSAPT, developed by the authors. XSAPT generalizes traditional dimer SAPT to many-body systems, so that a ligand-protein interaction (for example) can be separated into contributions from individual amino acids, reducing the cost of the calculation below that of even supramolecular DFT while retaining the accuracy of high-level ab initio quantum chemistry.This Account provides an overview of the SAPT0(KS) approach and the XSAPT family of methods. Several low-cost variants are described that provide accuracy approaching that of the best ab initio benchmarks yet are affordable enough to tackle ligand-protein binding and sizable host-guest complexes. These variants include SAPT+aiD, which uses ab initio atom-atom dispersion potentials ("+aiD") in place of second-order SAPT dispersion, and also SAPT+MBD, which incorporates many-body dispersion (MBD) effects that are important in the description of nanoscale materials. Applications to drug binding highlight the size-extensive nature of dispersion, which is not a weak interaction in large systems. Other applications highlight how a physics-based analysis can sometimes upend conventional wisdom regarding intermolecular forces. In particular, careful reconsideration of π-π interactions makes clear that the quadrupolar electrostatics (or "Hunter-Sanders") model of π-π stacking should be replaced by a "van der Waals model" in which conformational preferences arise from a competition between dispersion and Pauli repulsion. Our analysis also suggests that molecular shape, rather than aromaticity per se, is the key factor driving strong stacking interactions. Looking forward, we anticipate that XSAPT-based methods can play a role in screening of drug candidates and in materials design.

Rational Remodeling of Atypical Scaffolds for the Design of Photoswitchable Cannabinoid Receptor Tools

Azobenzene-embedded photoswitchable ligands are the widely used chemical tools in photopharmacological studies. Current approaches to azobenzene introduction rely mainly on the isosteric replacement of typical azologable groups. However, atypical scaffolds may offer more opportunities for photoswitch remodeling, which are chemically in an overwhelming majority. Herein, we investigate the rational remodeling of atypical scaffolds for azobenzene introduction, as exemplified in the development of photoswitchable ligands for the cannabinoid receptor 2 (CB2). Based on the analysis of residue-type clusters surrounding the binding pocket, we conclude that among the three representative atypical arms of the CB2 antagonist, AM10257, the adamantyl arm is the most appropriate for azobenzene remodeling. The optimizing spacer length and attachment position revealed AzoLig 9 with excellent thermal bistability, decent photopharmacological switchability between its two configurations, and high subtype selectivity. This structure-guided approach gave new impetus in the extension of new chemical spaces for tool customization for increasingly diversified photo-pharmacological studies and beyond.

Altered Peptide Self-Assembly and Co-Assembly with DNA by Modification of Aromatic Residues

Aromatic residues are widely used as building blocks for driving self-assemblies in natural and designer biomaterials. The noncovalent interactions involving aromatic rings determine proteins' structure and biofunction. Here, we studied the effects of changes in the proximity of the aromatic rings in a self-assembling peptide for modulating interactions involving the aromatic residues. By changing the distance between the aromatic ring and peptide backbone and replacing the side chain with a sulfur atom, we altered the nanostructures and gene transfection efficiency of peptide-DNA co-assemblies. This study demonstrates the significance of subtle alterations in aromatic interactions and facilitates deeper understanding of the aromatic-involving interactions.

'All That Glitters Is Not Gold': High-Resolution Crystal Structures of Ligand-Protein Complexes Need Not Always Represent Confident Binding Poses

Our understanding of the structure–function relationships of biomolecules and thereby applying it to drug discovery programs are substantially dependent on the availability of the structural information of ligand–protein complexes. However, the correct interpretation of the electron density of a small molecule bound to a crystal structure of a macromolecule is not trivial. Our analysis involving quality assessment of ~0.28 million small molecule–protein binding site pairs derived from crystal structures corresponding to ~66,000 PDB entries indicates that the majority (65%) of the pairs might need little (54%) or no (11%) attention. Out of the remaining 35% of pairs that need attention, 11% of the pairs (including structures with high/moderate resolution) pose serious concerns. Unfortunately, most users of crystal structures lack the training to evaluate the quality of a crystal structure against its experimental data and, in general, rely on the resolution as a ‘gold standard’ quality metric. Our work aims to sensitize the non-crystallographers that resolution, which is a global quality metric, need not be an accurate indicator of local structural quality. In this article, we demonstrate the use of several freely available tools that quantify local structural quality and are easy to use from a non-crystallographer’s perspective. We further propose a few solutions for consideration by the scientific community to promote quality research in structural biology and applied areas.

Screening of Anti-Lipase Components of Artemisia argyi Leaves Based on Spectrum-Effect Relationships and HPLC-MS/MS

Pancreatic lipase is a key lipase for triacylglyceride digestion and absorption, which is recognized as a promising target for treatment of metabolic disorders. Natural phytochemicals are hopeful sources for pancreatic lipase inhibitors. The leaves of Artemisia argyi H.Lév. and Vaniot (AL) is commonly used as herbal medicine or food supplement in China and other Asian countries for hundreds of years. AL mainly contains essential oils, phenolic acids, flavonoids and terpenoids, which exhibit many pharmacological activities such as antioxidant, anti-inflammatory, antimicrobial, analgetic, anti-cancer, anti-diabetes and immunomodulatory effects. However, the anti-lipase activity of AL was lack of study and the investigation of anti-lipase ingredients from AL was also insufficient. In the present study, the anti-lipase activity of AL was evaluated in vitro and the potentially pancreatic lipase inhibitors of AL were investigated. High performance liquid chromatography was used to establish fingerprints of AL samples, and fifteen peaks were selected. The anti-lipase activities of AL samples were evaluated by a pancreatic lipase inhibition assay. Then, the spectrum-effect relationships between fingerprints and pancreatic lipase inhibitory activities were investigated to identify the anti-lipase constitutes in AL. As the results, four caffeoylquinic acids, which were identified as neochlorogenic acid, chlorogenic acid, isochlorogenic acid B, and isochlorogenic acid A by high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry, were selected as potential pancreatic lipase inhibitors in AL. Moreover, anti-lipase activity assessment and molecular docking study of the four compounds were performed to validate the potential lipase inhibitors in AL. The results revealed that the four caffeoylquinic acids in AL as bioactive compounds displayed with anti-lipase activity. The present research provided evidences for the anti-lipase activity of AL, and suggested that some bioactive compounds in AL could be used as lead compounds for discovering of new pancreatic lipase inhibitors.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

Underappreciated Chemical Interactions in Protein-Ligand Complexes

Non-covalent interactions lie at the bases of the molecular recognition process. In medicinal chemistry, understanding how bioactive molecules interact with their target can help to explain structure-activity relationships (SAR) and improve potency of lead compounds. In particular, computational analysis of protein-ligand complexes can help to unravel key interactions and guide structure-based drug design.The literature describing protein-ligand complexes is typically focused on few types of non-covalent interactions (e.g., hydrophobic contacts, hydrogen bonds, and salt bridges). Stacking interactions involving aromatic rings are also relatively well known to medicinal chemistry practitioners. Potency optimization efforts are often focused on targeting these interactions. However, a variety of underappreciated interactions were shown to have a relevant effect on the stabilization of protein-ligand complexes. This chapter aims at listing selected non-covalent interactions and discuss some examples on how they can impact drug design.

In Silico Screening-Based Discovery of Novel Inhibitors of Human Cyclic GMP-AMP Synthase: A Cross-Validation Study of Molecular Docking and Experimental Testing

Cyclic GMP-AMP synthase (cGAS) has been recently uncovered to be a promising therapeutic target for immune-associated diseases. Until now, only a few inhibitors have been identified through high-throughput screening campaigns. Here, we reported the discovery of novel inhibitors for the catalytic domain of human cGAS (h-cGAS^CD) by virtual screening for the first time. To generate a reliable docking mode, we first obtained a high-resolution crystal structure of h-cGAS^CD in complex with PF-06928215, a known inhibitor of h-cGAS, followed by molecular dynamics simulations on this complex structure. Four fragment hits were identified by the virtual screening together with a thermal shift assay. The crystal structures of these four compounds in complex with h-cGAS^CD were subsequently determined, and the binding modes of the compounds were similar to those predicted by molecular docking, supporting the reliability of the docking model. In addition, an enzyme activity assay identified compound 18 (IC₅₀ = 29.88 ± 3.20 μM) from the compounds predicted by the virtual screening. A similarity search of compound 18 followed by a second virtual screening led to the discovery of compounds S2 (IC₅₀ = 13.1 ± 0.09 μM) and S3 (IC₅₀ = 4.9 ± 0.26 μM) as h-cGAS inhibitors with improved potency. Therefore, the present study not only provides the validated hit compounds for further development of h-cGAS inhibitors but also demonstrates a cross-validation study of virtual screening, in vitro experimental assays, and crystal structure determination.

Quantum Mechanical Investigation of Three-Dimensional Activity Cliffs Using the Molecules-in-Molecules Fragmentation-Based Method

The concept of activity cliff (AC) (i.e., a small structural modification resulting in a substantial bioactivity change) is widely encountered in medicinal chemistry during compound design. Whereas the study of ACs is of high interest as it provides a wealth of opportunities for effective drug design, its practical application in the actual drug development process has been difficult because of significant computational challenges. To provide some understanding of the ACs, we have carried out a rigorous quantum-mechanical investigation of the electronic interactions of a wide range of ACs (205 cliffs formed by 261 protein-ligand complexes covering 37 different receptor types) using multilayer molecules-in-molecules (MIM) fragmentation-based methodology. The MIM methodology enables performing accurate high-level quantum mechanical (QM) calculations at a substantially lower computational cost, while allowing for a quantitative decomposition of the protein-ligand binding energy into the contributions from individual residues, solvation, and entropy. Our investigation in this study is mainly focused on whether the QM binding energy calculation can correctly identify the higher potency cliff partner for a given ligand pair having a sufficiently high activity difference. We have also analyzed the effect of including crystal water molecules as a part of the receptor as well as the impact of ligand desolvation energy on the correct identification of the more potent ligand in a cliff pair. Our analysis reveals that, in the majority of the cases, the AC prediction could be significantly improved by carefully identifying the critical crystal water molecules, whereas the contribution from the ligand desolvation also remains essential. Additionally, we have exploited the residue-specific interaction energies provided by MIM to identify the key residues and interaction hot-spots that are responsible for the experimentally observed drastic activity changes. The results show that our MIM fragmentation-based protocol provides comprehensive interaction energy profiles that can be employed to understand the distinctiveness of ligand modifications, for potential applications in structure-based drug design.