Nirmatrelvir is an orally available inhibitor of SARS-CoV-2 main protease (Mpro) and the main ingredient of Paxlovid, a drug approved by the U.S. Food and Drug Administration for high-risk COVID-19 patients. Recently, a rare natural mutation, H172Y, was found to significantly reduce nirmatrelvir's inhibitory activity. As the COVID-19 cases skyrocket in China and the selective pressure of antiviral therapy builds in the US, there is an urgent need to characterize and understand how the H172Y mutation confers drug resistance. Here, we investigated the H172Y Mpro's conformational dynamics, folding stability, catalytic efficiency, and inhibitory activity using all-atom constant pH and fixed-charge molecular dynamics simulations, alchemical and empirical free energy calculations, artificial neural networks, and biochemical experiments. Our data suggest that the mutation significantly weakens the S1 pocket interactions with the N-terminus and perturbs the conformation of the oxyanion loop, leading to a decrease in the thermal stability and catalytic efficiency. Importantly, the perturbed S1 pocket dynamics weaken the nirmatrelvir binding in the P1 position, which explains the decreased inhibitory activity of nirmatrelvir. Our work demonstrates the predictive power of the combined simulation and artificial intelligence approaches, and together with biochemical experiments, they can be used to actively surveil continually emerging mutations of SARS-CoV-2 Mpro and assist the optimization of antiviral drugs. The presented approach, in general, can be applied to characterize mutation effects on any protein drug targets.

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries.

Staphylococcus aureus is a leading cause of life-threatening infections in the United States. It actively acquires the essential nutrient iron from human hemoglobin (Hb) using the iron-regulated surface-determinant (Isd) system. This process is initiated when the closely related bacterial IsdB and IsdH receptors bind to Hb and extract its hemin through a conserved tri-domain unit that contains two NEAr iron Transporter (NEAT) domains that are connected by a helical linker domain. Previously, we demonstrated that the tri-domain unit within IsdH (IsdH^N2N3) triggers hemin release by distorting Hb's F-helix. Here, we report that IsdH^N2N3 promotes hemin release from both the α- and β-subunits. Using a receptor mutant that only binds to the α-subunit of Hb and a stopped-flow transfer assay, we determined the energetics and micro-rate constants of hemin extraction from tetrameric Hb. We found that at 37 °C, the receptor accelerates hemin release from Hb up to 13,400-fold, with an activation enthalpy of 19.5 α 1.1 kcal/mol. We propose that hemin removal requires the rate-limiting hydrolytic cleavage of the axial HisF8 Nɛ–Fe³⁺ bond, which, based on molecular dynamics simulations, may be facilitated by receptor-induced bond hydration. Isothermal titration calorimetry experiments revealed that two distinct IsdH^N2N3·Hb protein·protein interfaces promote hemin release. A high-affinity receptor·Hb(A-helix) interface contributed ∼95% of the total binding standard free energy, enabling much weaker receptor interactions with Hb's F-helix that distort its hemin pocket and cause unfavorable changes in the binding enthalpy. We present a model indicating that receptor-introduced structural distortions and increased solvation underlie the IsdH-mediated hemin extraction mechanism.

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of μOR-opioid receptor (μOR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the μOR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of μOR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for μOR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln124^2.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a π-hole interaction with the conserved Tyr75^1.39. Our computational analysis provides new insights into the mechanism of μOR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.