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Organophosphonate-based nerve agents, such as VX, Sarin (GB), and Soman (GD), are among the most toxic chemicals to humankind. Recently, we have shown that Zr-based metal–organic frameworks (Zr-MOFs) can effectively catalyze the hydrolysis of these toxic chemicals for diminishing their toxicity. On the other hand, utilizing these materials in powder form is not practical, and developing scalable and economical processes for integrating these materials onto fibers is crucial for protective gear. Herein, we report a scalable, template-free, and aqueous solution-based synthesis strategy for the production of Zr-MOF-coated textiles. Among all MOF/fiber composites reported to date, the MOF-808/polyester fibers exhibit the highest rates of nerve agent hydrolysis. Moreover, such highly porous fiber composites display significantly higher protection time compared to that of its parent fabric for a mustard gas simulant, 2-chloroethyl ethyl sulfide (CEES). A decreased diffusion rate of toxic chemicals through the MOF layer can provide time needed for the destruction of the harmful species.

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Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) Surface Area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range 0.56~0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data while the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery.

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