For RQDs, increasing the CdS shell volume through the length elongation has no effect on either the peak wavelength or intensity of the CdSe core UV-vis absorption and ORF, but it reduces the QD fluorescence depolarization. In contrast, increasing CdS shell volume in the SQDs induces red-shift in the CdSe core peak UV-vis absorption and ORF wavelengths, and increases their peak cross-sections, but it has no effect on the SQD fluorescence depolarization. The RQD ORF cross-sections and quantum yields are significantly higher than their respective counterparts for the SQDs with similar particle sizes (volumes). While these new insights should be significant for the QD design, characterization, and applications, the methodology presented in this work is directly applicable for quantifying the optical activities of optically complex materials where the common UV-vis spectrometry and fluorescence spectroscopy are inadequate.We describe our design, synthesis, and chemical study of a set of functional epidithiodiketopiperazines (ETPs) and evaluation of their activity against five human cancer cell lines. Our structure-activity relationship-guided substitution of ETP alkaloids offers versatile derivatization while maintaining potent anticancer activity, offering exciting opportunity for their use as there are no examples of complex and potently anticancer (nM) ETPs being directly used as conjugatable probes or warheads. Our synthetic solutions to strategically designed ETPs with functional linkers required advances in stereoselective late-stage oxidation and thiolation chemistry in complex settings, including the application of novel reagents for dihydroxylation and cis-sulfidation of diketopiperazines. We demonstrate that complex ETPs equipped with a strategically substituted azide functional group are readily derivatized to the corresponding ETP-triazoles without compromising anticancer activity. Our chemical stability studies of ETPs along with cytotoxic evaluation of our designed ETPs against A549, DU 145, HeLa, HCT 116, and MCF7 human cancer cell lines provide insights into the impact of structural features on potency and chemical stability, informing future utility of ETPs in chemical and biological studies.The recent paradigm shift toward the use of the kinetics parameters in place of thermodynamic constants is leading the computational chemistry community to develop methods for studying the mechanisms of drug binding and unbinding. From this standpoint, molecular dynamics (MD) plays an important role in delivering insight at the molecular scale. However, a known limitation of MD is that the time scales are usually far from those involved in ligand-receptor unbinding events. Here, we show that the algorithm behind supervised MD (SuMD) can simulate the dissociation mechanism of druglike small molecules while avoiding the input of any energy bias to facilitate the transition. SuMD was tested on seven different intermolecular complexes, covering four G protein-coupled receptors the A2A and A1 adenosine receptors, the orexin 2 and the muscarinic 2 receptors, and the soluble globular enzyme epoxide hydrolase. SuMD well-described the multistep nature of ligand-receptor dissociation, rationalized previous experimental data and produced valuable working hypotheses for structure-kinetics relationships.Computational protein design remains a challenging task despite its remarkable success in the past few decades. With the rapid progress of deep-learning techniques and the accumulation of three-dimensional protein structures, the use of deep neural networks to learn the relationship between protein sequences and structures and then automatically design a protein sequence for a given protein backbone structure is becoming increasingly feasible. In this study, we developed a deep neural network named DenseCPD that considers the three-dimensional density distribution of protein backbone atoms and predicts the probability of 20 natural amino acids for each residue in a protein. selleck compound The accuracy of DenseCPD was 53.24 ± 0.17% in a 5-fold cross-validation on the training set and 55.53% and 50.71% on two independent test sets, which is more than 10% higher than those of previous state-of-the-art methods. Two approaches for using DenseCPD predictions in computational protein design were analyzed. The approach using the cutoff of accumulative probability had a smaller sequence search space compared with the approach that simply uses the top-k predictions and therefore enabled higher sequence identity in redesigning three proteins with Rosetta. The network and the datasets are available on a web server at http//protein.org.cn/densecpd.html. The results of this study may benefit the further development of computational protein design methods.Reversible and irreversible covalent ligands are advanced cysteine protease inhibitors in the drug development pipeline. K777 is an irreversible inhibitor of cruzain, a necessary enzyme for the survival of the Trypanosoma cruzi (T. cruzi) parasite, the causative agent of Chagas disease. Despite their importance, irreversible covalent inhibitors are still often avoided due to the risk of adverse effects. Herein, we replaced the K777 vinyl sulfone group with a nitrile moiety to obtain a reversible covalent inhibitor (Neq0682) of cysteine protease. Then, we used advanced experimental and computational techniques to explore details of the inhibition mechanism of cruzain by reversible and irreversible inhibitors. The isothermal titration calorimetry (ITC) analysis shows that inhibition of cruzain by an irreversible inhibitor is thermodynamically more favorable than by a reversible one. The hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) and Molecular Dynamics (MD) simulations were used to explore the mechanism of the reaction inhibition of cruzain by K777 and Neq0682. The calculated free energy profiles show that the Cys25 nucleophilic attack and His162 proton transfer occur in a single step for a reversible inhibitor and two steps for an irreversible covalent inhibitor. The hybrid QM/MM calculated free energies for the inhibition reaction correspond to -26.7 and -5.9 kcal mol-1 for K777 and Neq0682 at the MP2/MM level, respectively. These results indicate that the ΔG of the reaction is very negative for the process involving K777, consequently, the covalent adduct cannot revert to a noncovalent protein-ligand complex, and its binding tends to be irreversible. Overall, the present study provides insights into a covalent inhibition mechanism of cysteine proteases.