The malfunction of the methyl-CpG binding protein 2 (MeCP2) is associated with the Rett syndrome, one of the most common causes of cognitive impairment in females. MeCP2 is an intrinsically disordered protein (IDP), making its experimental characterization a challenge. There is currently no structure available for the full-length MeCP2 in any of the databases, and only the structure of its MBD domain has been solved. We used this structure to build a full-length model of MeCP2 by completing the rest of the protein via ab initio modeling. Using a combination of all-atom and coarse-grained simulations, we characterized its structure and dynamics as well as the conformational space sampled by the ID and transcriptional repression domain (TRD) domains in the absence of the rest of the protein. The present work is the first computational study of the full-length protein. Two main conformations were sampled in the coarse-grained simulations a globular structure similar to the one observed in the all-atom force field and a two-globule conformation. Our all-atom model is in good agreement with the available experimental data, predicting amino acid W104 to be buried, amino acids R111 and R133 to be solvent-accessible, and having a 4.1% α-helix content, compared to the 4% found experimentally. Finally, we compared the model predicted by AlphaFold to our Modeller model. The model was not stable in water and underwent further folding. Together, these simulations provide a detailed (if perhaps incomplete) conformational ensemble of the full-length MeCP2, which is compatible with experimental data and can be the basis of further studies, e.g., on mutants of the protein or its interactions with its biological partners.The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, data set, and seed bias impact the technology’s utility to medicinal and computational chemists. In this work, we analyze the impact of seed and training bias on the output of an activity-conditioned graph-based variational autoencoder (VAE). Leveraging a massive, labeled data set corresponding to the dopamine D2 receptor, our graph-based generative model is shown to excel in producing desired conditioned activities and favorable unconditioned physical properties in generated molecules. We implement an activity-swapping method that allows for the activation, deactivation, or retention of activity of molecular seeds, and we apply independent deep learning classifiers to verify the generative results. Overall, we uncover relationships between noise, molecular seeds, and training set selection across a range of latent-space sampling procedures, providing important insights for practical AI-driven molecule generation.Although antibodies are a powerful tool for molecular biology and clinical diagnostics, there are many emerging applications for which nucleic acid-based aptamers can be advantageous. However, generating high-quality aptamers with sufficient affinity and specificity for biomedical applications is a challenging feat for most research laboratories. In this Account, we describe four techniques developed in our laboratory to accelerate the discovery of high-quality aptamer reagents that can achieve robust binding even for challenging molecular targets. The first method is particle display, in which we convert solution-phase aptamers into aptamer particles that can be screened via fluorescence-activated cell sorting (FACS) to quantitatively isolate individual aptamer particles based on their affinity. This enables the efficient isolation of high-affinity aptamers in fewer selection rounds than conventional methods, thereby minimizing selection biases and reducing the emergence of artifacts in the final aptamer poo the flow-cell surface that incorporate alkyne-modified nucleobases and then performs a click reaction to couple those nucleobases to an azide-modified chemical moiety. This yields a sequence-defined array of tens of millions of base-modified sequences, which can then be characterized for affinity and specificity in a high-throughput fashion. Collectively, we believe that these advancements are helping to make aptamer technology more accessible, efficient, and robust, thereby enabling the use of these affinity reagents for a wider range of molecular recognition and detection-based applications.Fundamental understanding of the lithium-ion transport mechanism in polymer-inorganic composite electrolyte is crucially important for the rational design of composite electrolytes for solid-state batteries. In this work, the Li+ ion transport pathway in a model composite electrolyte of PEO containing sparsely dispersed LLZO (PEO-LLZO) was studied by an advanced characterization technique, i.e., 6Li-tracer NMR spectroscopy. By analyzing the 6Li distribution within the PEO-LLZO composite at the end of the discharge of an electrochemical cell of 6Li | PEO-LLZO | stainless steel with a fixed capacity (less than the total amount of the Li+ in the composite) at various current densities, it is found that the interfacial barrier between LLZO and PEO could cause a reduced Li+ flux through LLZO, particularly at high current densities, and therefore plays a critical role in determining the Li+ transport pathway in the composite electrolyte. This work provides an intuitive picture of Li+ ion transport in a polymer-inorganic composite electrolyte that is helpful to optimize and design better composite electrolytes.Potential dipeptidyl peptidase IV (DPP-IV) inhibitory oligopeptides from sorghum kafirin were developed using in silico and in vitro methodologies for the management of diabetes. Twenty-eight peptides with 5-10 residues were identified from the papain hydrolysates of sorghum kafirin. Sixteen nontoxic DPP-IV inhibitory peptides were screened with a computer method based on molecular docking. Molecular docking revealed that LPFYPQ (LP6), GPVTPPILG (GP9), and LPFYPQGV (LP8) effectively inactivated DPP-IV by binding to its active sites with a low interaction energy. An in silico analysis of these three inhibitory oligopeptides indicated that they were all bound to the S1 and S2 active pockets of DPP-IV through hydrogen bonds and hydrophobic interactions. The in vitro inhibitory activity was also verified. The DPP-IV inhibitory activities of LP6 and LP8 decreased after gastric digestion and remained stable after intestinal digestion, and the GP9 inhibitory activity remained stable after gastrointestinal digestion. Experimental results from Caco-2 cells showed further inhibitory effects of oligopeptides on DPP-IV. The results are relevant to the exploration of biofunctional DPP-IV inhibitory peptides from sorghum as a treatment for patients with diabetes or in medical research.To investigate the herbicidal potential of 2,5-diketopiperazines (2,5-DKPs), we applied a known protocol to produce a series of 2,5-DKPs through intramolecular N-alkylation of Ugi adducts. However, the method was not successful for the cyclization of adducts presenting aromatic rings with some substituents at the ortho position. Results from DFT calculations showed that the presence of voluminous groups at the ortho position of a benzene ring results in destabilization of the transition structure. Lower activation enthalpies for the SN2-type cyclization of Ugi adducts were obtained when bromine, instead of a chlorine anion, is the leaving group, indicating that the activation enthalpy for the cyclization step controls the formation of the 2,5-DKP. Some Ugi adducts and 2,5-DKPs formed crystals with suitable qualities for single-crystal X-ray diffraction data collection. Phytotoxic damage of some 2,5-DKPs on leaves of the weed Euphorbia heterophylla did not differ from those caused by the commercial herbicide diquat.Regulated cell death is a widely attractive subject among the topics of cancer therapy and has gained some advances for discovery of targeted anticancer drugs. In the past decade, nonapoptotic regulated cell death has been implicated in the development and therapeutic responses of a variety of human cancers. Hitherto, targeting autophagy-dependent cell death (ADCD), ferroptosis, and necroptosis with small molecules has been emerging as a hopeful strategy for the improvement of potential cancer therapy, which may have an advantage to bypass the apoptosis-resistance machinery. Thus, in this perspective, we concentrate on the key molecular insights into ADCD, ferroptosis, and necroptosis and summarize the corresponding small molecules in potential cancer therapy. Moreover, the relationships between the three subroutines and small molecules modulating the crosstalk are discussed. We believe that these inspiring findings would be advantageous to exploiting more potential targets and pharmacological small molecules in future cancer treatment.Previously, we developed a rhodium-catalyzed [5 + 2 + 1] cycloaddition of ene-vinylcyclopropanes (ene-VCPs) and carbon monoxide to synthesize eight-membered carbocycles. The efficiency of this reaction can be appreciated from its application in the synthesis of several natural products. Herein we report the results of a 15-year investigation into the mechanism of the [5 + 2 + 1] cycloaddition by applying visual kinetic analysis and high-level quantum chemical calculations at the DLPNO-CCSD(T)//BMK level. According to the kinetic measurements, the resting state of the catalyst possesses a dimeric structure (with two rhodium centers) whereas the active catalytic species is monomeric (with one rhodium center). The catalytic cycle consists of cyclopropane cleavage (the turnover-limiting step), alkene insertion, CO insertion, reductive elimination, and catalyst transfer steps. Other reaction pathways have also been considered but then have been ruled out. The steric origin of the diastereoselectivity (cis versus trans) was revealed by comparing the alkene insertion transition states. In addition, how the double-bond configuration of the VCPs (Z versus E) affects the substrate reactivity and the origins of chemoselectivity ([5 + 2 + 1] versus [5 + 2]) were also investigated. The present study will provide assistance in understanding other carbonylative annulations catalyzed by transition metals.The future of the energy industry and green transportation critically relies on exploration of high-performance, reliable, low-cost, and environmentally friendly energy storage and conversion materials. Understanding the chemical processes and phenomena involved in electrochemical energy storage and conversion is the premise of a revolutionary materials discovery. In this article, we review the recent advancements of application of state-of-the-art vibrational spectroscopic techniques in unraveling the nature of electrochemical energy, including bulk energy storage, dynamics of liquid electrolytes, interfacial processes, etc. Technique-wise, the review covers a wide range of spectroscopic methods, including classic vibrational spectroscopy (direct infrared absorption and Raman scattering), external field enhanced spectroscopy (surface enhanced Raman and IR, tip enhanced Raman, and near-field IR), and two-photon techniques (2D infrared absorption, stimulated Raman, and vibrational sum frequency generation). Finally, we provide perspectives on future directions in refining vibrational spectroscopy to contribute to the research frontier of electrochemical energy storage and conversion.