Emerging SARS‑CoV‑2 variants and related zoonotic sarbecoviruses rely on ACE2 for cell entry, motivating host‑directed antivirals that block spike-ACE2 interaction. Here, we characterize MB‑32, a benzothiazole small molecule that binds ACE2, selectively disrupts binding of SARS‑CoV‑2 spike receptor‑binding domain to ACE2, and preserves ACE2 enzymatic activity across species. MB‑32 potently inhibits entry of SARS‑CoV‑2 variants, SARS‑CoV‑1 and diverse bat/pangolin sarbecoviruses in ACE2‑expressing cells, while sparing vesicular stomatitis virus and authentic MERS‑CoV, indicating non‑virucidal, ACE2‑focused activity. Biochemical and biophysical analyses, supported by ACE2 mutagenesis, support a model in which MB‑32 engages a non‑catalytic surface pocket on the ACE2 N‑terminal helix to allosterically disrupt spike attachment. Intranasal MB‑32 achieves high airway concentrations, protects male ACE2‑transgenic mice and hamsters from SARS‑CoV‑2 disease, and prevents contact transmission of Omicron‑lineage viruses without detectable cardiovascular toxicity. These findings establish MB‑32 as a host‑targeted ACE2 entry inhibitor and provide a framework for small‑molecule ACE2‑directed antivirals against current and future sarbecovirus spillovers.

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

In both cancer and infections, diseased cells are presented to human Vγ9Vδ2 T cells through an ‘inside out’ signalling process whereby structurally diverse phosphoantigen (pAg) molecules are sensed by the intracellular domain of butyrophilin BTN3A1 1 – 4 . Here we show how—in both humans and alpaca—multiple pAgs function as ‘molecular glues’ to promote heteromeric association between the intracellular domains of BTN3A1 and the structurally similar butyrophilin BTN2A1. X-ray crystallography studies visualized that engagement of BTN3A1 with pAgs forms a composite interface for direct binding to BTN2A1, with various pAg molecules each positioned at the centre of the interface and gluing the butyrophilins with distinct affinities. Our structural insights guided mutagenesis experiments that led to disruption of the intracellular BTN3A1–BTN2A1 association, abolishing pAg-mediated Vγ9Vδ2 T cell activation. Analyses using structure-based molecular-dynamics simulations, 19 F-NMR investigations, chimeric receptor engineering and direct measurement of intercellular binding force revealed how pAg-mediated BTN2A1 association drives BTN3A1 intracellular fluctuations outwards in a thermodynamically favourable manner, thereby enabling BTN3A1 to push off from the BTN2A1 ectodomain to initiate T cell receptor–mediated γδ T cell activation. Practically, we harnessed the molecular-glue model for immunotherapeutics design, demonstrating chemical principles for developing both small-molecule activators and inhibitors of human γδ T cell function.

Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.

Human Vγ9Vδ2 T cells respond to microbial infections and malignancy by sensing diphosphate-containing metabolites called phosphoantigens, which bind to the intracellular domain of butyrophilin 3A1, triggering extracellular interactions with the Vγ9Vδ2 T cell receptor (TCR). Here, we examined the molecular basis of this “inside-out” triggering mechanism. Crystal structures of intracellular butyrophilin 3A proteins alone or in complex with the potent microbial phosphoantigen HMBPP or a synthetic analog revealed key features of phosphoantigens and butyrophilins required for γδ T cell activation. Analyses with chemical probes and molecular dynamic simulations demonstrated that dimerized intracellular proteins cooperate in sensing HMBPP to enhance the efficiency of γδ T cell activation. HMBPP binding to butyrophilin doubled the binding force between a γδ T cell and a target cell during “outside” signaling, as measured by single-cell force microscopy. Our findings provide insight into the “inside-out” triggering of Vγ9Vδ2 T cell activation by phosphoantigen-bound butyrophilin, facilitating immunotherapeutic drug design.

Because protein-protein interactions underpin most biological processes, developing tools that target them to understand their function or to inform the development of therapeutics is an important task. SUMOylation is the posttranslational covalent attachment of proteins in the SUMO family (SUMO-1, SUMO-2, or SUMO-3), and it regulates numerous cellular pathways. SUMOylated proteins are recognized by proteins with SUMO-interaction motifs (SIMs) that facilitate noncovalent interactions with SUMO. We describe the use of the Affimer system of peptide display for the rapid isolation of synthetic binding proteins that inhibit SUMO-dependent protein-protein interactions mediated by SIMs both in vitro and in cells. Crucially, these synthetic proteins did not prevent SUMO conjugation either in vitro or in cell-based systems, enabling the specific analysis of SUMO-mediated protein-protein interactions. Furthermore, through structural analysis and molecular modeling, we explored the molecular mechanisms that may underlie their specificity in interfering with either SUMO-1-mediated interactions or interactions mediated by either SUMO-2 or SUMO-3. Not only will these reagents enable investigation of the biological roles of SUMOylation, but the Affimer technology used to generate these synthetic binding proteins could also be exploited to design or validate reagents or therapeutics that target other protein-protein interactions.

The stability of folded proteins is critical to their biological function and for the efficacy of protein therapeutics. Predicting the energetic effects of protein mutations can improve our fundamental understanding of structural biology, the molecular basis of diseases, and possible routes to addressing those diseases with biological drugs. Identifying the effect of single amino acid point mutations on the thermodynamic equilibrium between the folded and unfolded states of a protein can pinpoint residues of critical importance that should be avoided in the process of improving other properties (affinity, solubility, viscosity, etc.) and suggest changes at other positions for increasing stability in protein engineering. Multiple computational tools have been developed for in silico predictions of protein stability in recent years, ranging from sequence-based empirical approaches to rigorous physics-based free energy methods. In this work, we show that FEP+, which is a free energy perturbation method based on all-atom molecular dynamics simulations, can provide accurate thermal stability predictions for a wide range of biologically relevant systems. Significantly, the FEP+ approach, while originally developed for relative binding free energies of small molecules to proteins and not specifically fitted for protein stability calculations, performs well compared to other methods that were fitted specifically to predict protein stability. Here, we present the broadest validation of a rigorous free energy-based approach applied to protein stability reported to date: 700+ single-point mutations spanning 10 different protein targets. Across the entire data set, we correctly classify the mutations as stabilizing or destabilizing in 84% of the cases, and obtain statistically significant predictions as compared with experiment [average error of ~1.6kcal/mol and coefficient of determination (R2) of 0.40]. This study demonstrates, for the first time in a large-scale validation, that rigorous free energy calculations can be used to predict changes in protein stability from point mutations without parameterization or system-specific customization, although further improvements should be possible with additional sampling and a better representation of the unfolded state of the protein. Here, we describe the FEP+ method as applied to protein stability calculations, summarize the large-scale retrospective validation results, and discuss limitations of the method, along with future directions for further improvements.

Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. Methodology/results: The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. Conclusion: AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

3-D ligand conformations are required for most ligand-based drug design methods, such as pharmacophore modeling, shape-based screening, and 3-D QSAR model building. Many studies of conformational search methods have focused on the reproduction of crystal structures (i.e. bioactive conformations); however, for ligand-based modeling the key question is how to generate a ligand alignment that produces the best results for a given query molecule. In this work, we study different conformation generation modes of ConfGen and the impact on virtual screening (Shape Screening and e-Pharmacophore) and QSAR predictions (atom-based and field-based). In addition, we develop a new search method, called common scaffold alignment, that automatically detects the maximum common scaffold between each screening molecule and the query to ensure identical coordinates of the common core, thereby minimizing the noise introduced by analogous parts of the molecules. In general, we find that virtual screening results are relatively insensitive to the conformational search protocol; hence, a conformational search method that generates fewer conformations could be considered “better” because it is more computationally efficient for screening. However, for 3-D QSAR modeling we find that more thorough conformational sampling tends to produce better QSAR predictions. In addition, significant improvements in QSAR predictions are obtained with the common scaffold alignment protocol developed in this work, which focuses conformational sampling on parts of the molecules that are not part of the common scaffold.

Taking into consideration the influence of intermediate principal stress on deep-buried circular tunnels,and under plane strain hypothesis,the unified strength criterion was precisely matched to the Drucker-Prager strength criterion.The analytical expression of intermediate principal stress in plastic state for the perfect elastoplastic material with dilatancy was derived from the Drucker-Prager strength criterion.Combining the analytical expression of intermediate principal stress and non-associated flow rule,unified solutions for stress and strain of the tunnel plastic area were obtained.The result shows that the classic solutions of Kastner are special cases of the unified solutions.A practical engineering instance was given according to the unified solutions and the regularities which intermediate and dilation angle act on the surrounding rock of the deep-buried circular tunnel plastic area were discussed.

The hydroxy-aluminum/polyaluminum modified clay and the clay only samples were examined by X-ray diffraction(XRD),unconconsolidated undrained(UU) triaxial strength test,unconfined compressive strength testing techniques,swelling potential tests and so on.The results indicated that the hydrophilic nature and swelling-shrinkage property were changed drastically,that the free swelling ratio(F_s)could be reduced sharply,and that the particle size gradation,strength characteristics of the modified montmorillonite clay were improved greatly.The clay content decreased dramatically,while the silt content increased.This study suggests that the hydroxy-aluminum/polyaluminum can influence the mechanical and physical properties of montmorillonite clay by physicochemical interaction.

Virtual screening is a widely used strategy in modern drug discovery and 2D fingerprint similarity is an important tool that has been successfully applied to retrieve active compounds from large datasets. However, it is not always straightforward to select an appropriate fingerprint method and associated settings for a given problem. Here, we applied eight different fingerprint methods, as implemented in the new cheminformatics package Canvas, on a well-validated dataset covering five targets. The fingerprint methods include Linear, Dendritic, Radial, MACCS, MOLPRINT2D, Pairwise, Triplet, and Torsion. We find that most fingerprints have similar retrieval rates on average; however, each has special characteristics that distinguish its performance on different query molecules and ligand sets. For example, some fingerprints exhibit a significant ligand size dependency whereas others are more robust with respect to variations in the query or active compounds. In cases where little information is known about the active ligands, MOLPRINT2D fingerprints produce the highest average retrieval actives. When multiple queries are available, we find that a fingerprint averaged over all query molecules is generally superior to fingerprints derived from single queries. Finally, a complementarity metric is proposed to determine which fingerprint methods can be combined to improve screening results.

Virtual screening of a library of commercially available compounds versus the structure of Mycobacterium tuberculosis lumazine synthase identified 2-(2-oxo-1,2-dihydrobenzo[cd]indole-6-sulfonamido)acetic acid ( 9) as a possible lead compound. Compound 9 proved to be an effective inhibitor of M. tuberculosis lumazine synthase with a K-i of 70 mu M. Lead optimization through replacement of the carboxymethylsulfonamide sidechain with sulfonamides substituted with alkyl phosphates led to a four-carbon phosphate 38 that displayed a moderate increase in enzyme inhibitory activity (K-i 38 mu M). Molecular modeling based on known lumazine synthase/inhibitor crystal structures suggests that the main forces stabilizing the present benzindolone/enzyme complexes involve pi-pi stacking interactions with Trp27 and hydrogen bonding of the phosphates with Arg128, the backbone nitrogens of Gly85 and Gln86, and the side chain hydroxyl of Thr87. (C) 2010 Elsevier Ltd. All rights reserved.

The enzymes in the nucleotide pyrophosphatase/phosphodiesterase (NPP) family have various substrates such as nucleotides, phospholipids, and sphingolipids. The substrate specificity in relation to their structures is largely unknown because no mammalian NPP complex has been crystallized. NPP7, also called alkaline sphingomyelinase (alk-SMase), is a NPP family member that may have important implications in carcinogenesis and cholesterol absorption. The sequence of NPP7 is 36% similar to that of the closest NPP member, but NPP7 has no activity against nucleotides. In this work, we predict the three-dimensional structure of NPP7 by homology modeling using a recently crystallized NPP from bacteria. Using the model, we studied the substrate specificity of the enzyme by docking. The model generated explains the functional changes in previous mutagenesis studies and rationalizes the structural basis for the lack of activity toward nucleotides. An effort to shift the substrate specificity from sphingomyelin (SM) to nucleotide was not successful but revealed a site-directed mutation that increased activity toward SM. In conclusion, this is the first study to predict the structure of a mammalian NPP and its substrate specificity by molecular modeling. The information may be helpful in understanding the functional differences of NPP members.

The use of 3-dimensional molecular shapes has been demonstrated to be useful in comparing small molecules.We have developed a novel method that allows for rapid superposition and scoring of flexible molecules.

In this study, we characterise the binding site of the human N-methyl-d-aspartate (NMDA) receptor subunit NR3A. Saturation radioligand binding of the NMDA receptor agonists [(3)H]-glycine and [(3)H]-glutamate showed that only glycine binds to human NR3A (hNR3A) with high affinity (K(d)=535nM (277-793nM)). Eight amino acids, which correspond to amino acids that are critical for ligand binding to other NMDA receptor subunits, situated within the S1S2 predicted ligand binding domain of hNR3A were mutated, which resulted in complete or near complete loss of [(3)H]-glycine binding to hNR3A. The NMDA NR1 glycine site agonist d-serine and partial agonist HA-966 (3-amino-1-hydroxypyrrolid-2-one), similarly to glycine displaced [(3)H]-glycine monophasically, suggesting a single common binding site. However, neither the partial agonist d-cycloserine nor the antagonist 7-chlorokynurenic acid displaced [(3)H]-glycine. Using homology modelling, a model of the NR3A binding pocket was generated which we suggest can be used to identify candidate agonists and antagonists. Our data show that glycine is a ligand, and most probably the endogenous ligand, for native NR3A at a binding site with unique pharmacological characteristics.

Zinc ions are frequently found in DNA-binding proteins. p53 is a cancer-related transcriptional factor, and its DNA-binding domain (DBD) contains a Zn2+, which has been shown to be important for aggregation and sequence-specific DNA binding. We have carried out molecular dynamics simulations to investigate the influence of Zn2+ on the p53 DNA recognition and the stability of the DBD. In the simulation with Zn2+ present, the protein attracted to the DNA phosphate backbone, allowing for Arg248 on loop L3 to be inserted into the minor groove for specific contact with the DNA base. The insertion of Arg248 between the backbone phosphate groups in the minor groove caused a narrowing of the minor groove, which is not seen in the simulation without Zn2+. Structurally, the zinc ion coordinated the motions among the different protein structural elements, which could also be important for optimal binding and core packing. The influence of Zn2+ on protein stability was mainly localized to the L2 loop. Our results suggest that L2 may be a frustrated and highly flexible element and play an important role in aggregation of Zn-free p53. Zn2+ keeps the L2 structured and probably prevents aggregation.