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MSC Making with regard to School Many studies: From a Clinical-Grade into a Entire GMP-Compliant Procedure.
Membraneless organelles are dynamical cellular condensates formed via biomolecular liquid-liquid phase separation of proteins and RNA molecules. Multiple evidence suggests that in several cases disordered proteins are structural scaffolds that drive the condensation by forming a dynamic network of inter- and intramolecular contacts. Despite the blooming research activity in this field, the structural characterization of these entities is very limited, and we still do not understand how the phase behavior is encoded in the amino acid sequences of the scaffolding proteins. Here we exploited explicit-solvent atomistic simulations to investigate the N-terminal disordered region of DEAD-box helicase 4 (NDDX4), which is a well-established model for phase separation. Notably, we determined NDDX4 conformational ensemble at the single-molecule level, and we relied on a "divide-and-conquer" strategy, based on simulations of various protein fragments at high concentration, to probe intermolecular interactions in conditions mimicking real condensates. Our results provide a high-resolution picture of the molecular mechanisms underlying phase separation in agreement with NMR and mutagenesis data and suggest that clusters of arginine and aromatic residues may stabilize the assembly of several condensates.The ab initio calculation of exact quantum reaction rate constants comes at a high cost due to the required dynamics of reactants on multidimensional potential energy surfaces. In turn, this impedes the rapid design of the kinetics for large sets of coupled reactions. In an effort to overcome this hurdle, a deep neural network (DNN) was trained to predict the logarithm of quantum reaction rate constants multiplied by their reactant partition function-rate products. The training dataset was generated in-house and contains ∼1.5 million quantum reaction rate constants for single, double, symmetric and asymmetric one-dimensional potentials computed over a broad range of reactant masses and temperatures. GSK-2879552 in vitro The DNN was able to predict the logarithm of the rate product with a relative error of 1.1%. Furthermore, when comparing the difference between the DNN prediction and classical transition state theory at temperatures below 300 K a relative percent error of 31% was found with respect to the exact difference. Systems beyond the test set were also studied, these included the H + H2 reaction, the diffusion of hydrogen on Ni(100), the Menshutkin reaction of pyridine with CH3Br in the gas phase, the reaction of formalcyanohydrin with HS- in water and the F + HCl reaction. For these reactions, the DNN predictions were accurate at high temperatures and in good agreement with the exact rates at lower temperatures. This work shows that one can take advantage of a DNN to gain insight on reactivity in the quantum regime.Repeated column chromatography of Syringa dilatata flowers, a native shrub to Korea, led to the isolation of eight new oleoside-type secoiridoids, syringoleosides A-H (1-8), as well as five known secoiridoids (9-13). The new chemical structures were identified through spectroscopic data analysis, as well as the application of chemical methods. Compounds 1, 2, 6, 7, 11, and 13 showed suppression effects on NO production in LPS-induced RAW 264.7 cells, with IC50 values ranging from 32.5 ± 9.8 to 65.7 ± 11.0 μM, and no visible toxicity. The content of the major secoiridoids in S. dilatata flowers, compounds 1, 4, 5, 8, 9, 12, and 13, were determined through HPLC analysis.Immune checkpoint blockade is one of the most promising strategies of cancer immunotherapy. However, unlike classical targeted therapies, it is currently solely based on expensive monoclonal antibodies, which often inflict immune-related adverse events. Herein, we propose a novel small-molecule inhibitor targeted at the most clinically relevant immune checkpoint, PD-1/PD-L1. The compound is capable of disrupting the PD-1/PD-L1 complex by antagonizing PD-L1 and, therefore, restores activation of T cells similarly to the antibodies, while being cheap in production and possibly nonimmunogenic. The final compound is significantly smaller than others reported in the literature while being nontoxic to cells even at high concentrations. The scaffold was designed using a structure-activity relationship screening cascade based on a new antagonist-induced dissociation NMR assay, called the weak-AIDA-NMR. Weak-AIDA-NMR finds true inhibitors, as opposed to only binders to the target protein, in early steps of lead compound development, and this process makes it less time and cost consuming.Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.
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