Jing An

G protein-coupled receptors (GPCRs) are transmembrane proteins that mediate diverse signaling functions, making them important therapeutic targets. The chemokine receptor CXCR4, a GPCR, plays multifaceted roles in both normal physiological and pathological processes. Here, we constructed conformational free energy profiles of CXCR4 activation using Targeted Molecular Dynamics (TMD) and Molecular Dynamics (MD) simulations combined with our refined Coarse-Grained (CG) model for membrane proteins. The simulations revealed that CXCR4 activation involves three distinct transition states. TS1, which exhibits the highest activation energy barrier, primarily involves conformational changes in intracellular loop 3 (ICL3) and the prerearrangement of transmembrane helices TM5 and TM6. Additionally, the stabilization of the specific active conformations of W94 and E288 within the CXCR4 active site was found to reduce the activation energy barriers of TS2 and TS3, respectively. Alanine scanning further revealed the dynamic roles of other residues whose putative crucial role in CXCR4 activation had previously been postulated, transitioning from stabilizing the inactive state to facilitating activation in later stages. Guided by these computational insights, we designed a small-molecule compound, HL82624, using a dual-moiety strategy combining the first two amino acids of SDF-1α (Lys and Pro) with the CXCR4 antagonist HF51116. Competitive binding and cell migration assays showed that HL82624 binds to CXCR4 and effectively triggers cell migration, confirming its activity as a CXCR4 agonist. Taken together, this study provides mechanistic insight into CXCR4 activation and a computational strategy for the rational design of small-molecule CXCR4 agonists.

Proteases are versatile therapeutic targets for a wide variety of human diseases, including cancer, cardiovascular diseases, and infections by viruses, bacteria, and parasites. The main protease (M ^pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), has been one of the major drug targets for the treatment of COVID-19. In this study, a small-molecule covalent inhibitor, H102 , of SARS-CoV-2 M ^pro was developed from serial structural modifications on a compound previously reported by us to have anti-SARS-CoV-2 activity. H102 exhibited very high potency in SARS-CoV-2 M ^pro inhibition with IC ₅₀ of 8.8 nM and strongly prevented SARS-CoV-2 replication in VeroE6 cells. The co-crystal structure of H102 bound to M ^pro determined at 1.50 Å resolution provided a structural mechanism of H102 ’s action and revealed an unusual distortion of the catalytic dyad of the viral enzyme which was caused by the benzyl ring of P2 position of H102 interacting with the side chain of catalytic dyad His41 residue of M ^pro and pulling His41 side chain away from the other catalytic dyad Cys145 residue. This structural mechanism of H102 is very different from that of other reported covalent M ^pro inhibitors for which His41 side chain orientation and its close proximity to Cys145 remain unchanged with the binding of a covalent inhibitor. As such, H102 may serve as a biochemical probe for investigating further an unusual mechanism of the viral enzyme’s catalytic dyad disruption and inhibition, and as a prototype distinct from other reported covalent inhibitors for the development of novel antiviral agents. IMPORTANCE: A nanomolar potent small-molecule inhibitor, H102 , of SARS-CoV-2 M ^pro was developed and exhibited strong anti-SARS-CoV-2 infection activity in cells. Co-crystal structure determination of its complex with M ^pro provided a structural mechanism of H102 ’s action and revealed an interesting structural feature: the benzyl ring at the P2 position of H102 interacts with the reorientated His41 side chain, accompanied by a significant increase of the distance between the catalytic dyad Cys145-His41 residues, which is uncommon in reported covalent inhibitors. Compound H102 may be used as a biochemical probe to further investigate mechanisms of M ^pro inhibition and potentially different type of lead for developing antiviral agents for treating disease caused by novel coronavirus SARS-CoV-2.

The main protease (M^pro) is essential for the replication of SARS-CoV-2, making it one of the major therapeutic targets for COVID-19 treatment. Here, we explored the conformational dynamics and energetics of the catalytic residue His41 in M^pro, as revealed by a rare conformational shift observed in the cocrystal structures of M^pro bound by certain inhibitors. Using steered molecular dynamics combined with umbrella sampling, we demonstrated that π-cation interactions between these inhibitors and the ionized catalytic dyad significantly reduced the energy barrier for the conformational flip of the His41 side chain. To further investigate the structure-activity relationship linked to this conformational change, we designed and synthesized a series of covalent inhibitors that control His41 flipping. Among these, compound H102-7 exhibited remarkable inhibitory activity with an IC₅₀ of 5 nM. Drug resistance studies revealed that these inhibitors displayed improved resistance profiles compared to the clinically approved M^pro covalent inhibitor, Nirmatrelvir. This study integrates computational simulations, medicinal chemistry, and molecular biology to uncover an interesting allosteric effect of a key catalytic residue of SARS-CoV-2 M^pro and yields new promising molecules for the further development of M^pro-targeted therapeutic intervention.

Many reported proteasome inhibitors, including the three clinically approved inhibitors, bortezomib, carfilzomib, and ixazomib, have peptidic structures. In this study, using a hybrid and versatile strategy for covalent virtual screening by combining warhead screening and preprocessing with GOLD and CovDock software that were applied to the ZINC virtual library, we identified multiple proteasome inhibitors with new nonpeptidic structural scaffolds. Proteasome inhibition assays confirmed the inhibitory activities of these new compounds. These results demonstrate the effectiveness of our computational strategy for large-scale covalent virtual screening. Furthermore, these identified proteasome inhibitors may serve as starting points for the development of a new class of nonpeptidic therapeutic agents.

The chemokine receptor CXCR4 has become an attractive therapeutic target for HIV-1 infection, hematopoietic stem cell mobilization, and cancer metastasis. A wide variety of synthetic antagonists of CXCR4 have been developed and studied for a growing list of clinical applications. To compare the biological effects of different antagonists on CXCR4 functions and their common and/or distinctive molecular interactions with the receptor, we conducted head-to-head comparative cell-based biological and mutational analyses of the interactions with CXCR4 of eleven reported antagonists, including HC4319, DV3, DV1, DV1 dimer, V1, vMIP-II, CVX15, LY2510924, IT1t, AMD3100, and AMD11070 that were representative of different structural classes of D-peptides, L-peptide, natural chemokine, cyclic peptides, and small molecules. The results were rationalized by molecular modeling of CXCR4–antagonist interactions from which the common as well as different receptor binding sites of these antagonists were derived, revealing a number of important residues such as W94, D97, H113, D171, D262, and E288, mostly of negative charge. To further examine this finding, we designed and synthesized new antagonistic analogs by adding positively charged residues Arg to a D-peptide template to enhance the postulated charge–charge interactions. The newly designed analogs displayed significantly increased binding to CXCR4, which supports the notion that negatively charged residues of CXCR4 can engage in interactions with moieties of positive charge of the antagonistic ligands. The results from these mutational, modeling and new analog design studies shed new insight into the molecular mechanisms of different types of antagonists in recognizing CXCR4 and guide the development of new therapeutic agents.

Human immunodeficiency virus-1 (HIV-1) recognizes one of its principal coreceptors, CXC chemokine receptor 4 (CXCR4), on the host cell via the third variable loop (V3 loop) of HIV-1 envelope glycoprotein gp120 during the viral entry process. Here, the mechanism of the molecular recognition of HIV-1 gp120 V3 loop by coreceptor CXCR4 was probed by synthetic peptides containing the full-length V3 loop. The two ends of the V3 loop were covalently linked by a disulfide bond to form a cyclic peptide with better conformational integrity. In addition, to probe the effect of the changed side-chain conformations of the peptide on CXCR4 recognition, an all-D-amino acid analog of the L-V3 loop peptide was generated. Both of these cyclic L- and D-V3 loop peptides displayed comparable binding recognition to the CXCR4 receptor, but not to another chemokine receptor, CCR5, suggesting their selective interactions with CXCR4. Molecular modeling studies revealed the important roles played by many negative-charged Asp and Glu residues on CXCR4 that probably engaged in favorable electrostatic interactions with the positive-charged Arg residues present in these peptides. These results support the notion that the HIV-1 gp120 V3 loop-CXCR4 interface is flexible for ligands of different chiralities, which might be relevant in terms of the ability of the virus to retain coreceptor recognition despite the mutations at the V3 loop.

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.