Structure of polyamidoamide dendrimers up to limiting generations : a mesoscale description

The polyamidoamide (PAMAM) class of dendrimers was one of the first dendrimers synthesized by Tomalia and co-workers at Dow. Since its discovery the PAMAMs have stimulated many discussions on the structure and dynamics of such hyperbranched polymers. Many questions remain open because the huge conformation disorder combined with very similar local symmetries have made it difficult to characterize experimentally at the atomistic level the structure and dynamics of PAMAM dendrimers. The higher generation dendrimers have also been difficult to characterize computationally because of the large size (294852 atoms for generation 11) and the huge number of conformations. To help provide a practical means of atomistic computational studies, we have developed an atomistically informed coarse-grained description for the PAMAM dendrimer. We find that a two-bead per monomer representation retains the accuracy of atomistic simulations for predicting size and conformational complexity, while reducing the degrees of freedom by tenfold. This mesoscale description has allowed us to study the structural properties of PAMAM dendrimer up to generation 11 for time scale of up to several nanoseconds. The gross properties such as the radius of gyration compare very well with those from full atomistic simulation and with available small angle x-ray experiment and small angle neutron scattering data. The radial monomer density shows very similar behavior with those obtained from the fully atomistic simulation. Our approach to deriving the coarse-grain model is general and straightforward to apply to other classes of dendrimers

Predicted Structures and Dynamics for Agonists and Antagonists Bound to Serotonin 5-HT2B and 5-HT2C Receptors

Subtype 2 serotonin (5-hydroxytryptamine, 5-HT) receptors are major drug targets for schizophrenia, feeding disorders, perception, depression, migraines, hypertension, anxiety, hallucinogens, and gastrointestinal dysfunctions.' We report here the predicted structure of 5-HT2B and 5-HT2C receptors bound to highly potent and selective 5-HT2B antagonist PRX-08066 3, (pKi: 30 nM), including the key binding residues [V103 (2.53), L132 (3.29), V190 (4.60), and L347 (6.58)] determining the selectivity of binding to 5-HT2B over 5-HT2A. We also report structures of the endogenous agonist (5 HT) and a HT2B selective antagonist 2 (1-methyl-1-1,6,7,8-tetrahydro-pyrrolo [2,3-g]quinoline-5-carboxylic acid pyridine-3-ylamide). We examine the dynamics for the agonist-and antagonist-bound HT2B receptors in explicit membrane and water finding dramatically different patterns of water migration into the NPxxY motif and the binding site that correlates with the stability of ionic locks in the D(E)RY region

Pb-activated Amine-assisted Photocatalytic Hydrogen Evolution Reaction on Organic-Inorganic Perovskites

We report here the reaction mechanism for explicit aqueous solvent quantum mechanics (QM) studies determining the energetics and reaction barriers for the photocatalytic hydrogen evolution reaction (HER) on CH_3NH_3PbI_3 surface. We find that both the lead (Pb) atoms and the surface organic molecules play essential roles, leading to a two-step Pb-activated amine-assisted (PbAAA) reaction mechanism involving an intermediate lead hydride state. Both H of H_2 product are extracted from surface organic molecules, while two protons from the solution migrate along water chains via the Grotthuss mechanism to replace the H in organic molecule. We obtain a reaction barrier of 1.08 eV for photochemical generation of H_2 on CH_3NH_3PbI_3 compared to 2.61 eV for the dark reaction. We expect this HER mechanism can also apply to the other organic perovskites, but the energy barriers and reaction rates may depend on the basicity of electrolyte and intrinsic structures of perovskites

ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. In silico models for predicting the percentage of HIA based on calculated molecular descriptors are highly needed for the rapid estimation of this property. Here, we have studied the performance of a support vector machine (SVM) to classify compounds with high or low fractional absorption (%FA > 30% or %FA ≤ 30%). The analyzed data set consists of 578 structural diverse druglike molecules, which have been divided into a 480-molecule training set and a 98-molecule test set. Ten SVM classification models have been generated to investigate the impact of different individual molecular properties on %FA. Among these studied important molecule descriptors, topological polar surface area (TPSA) and predicted apparent octanol−water distribution coefficient at pH 6.5 (logD_(6.5)) show better classification performance than the others. To obtain the best SVM classifier, the influences of different kernel functions and different combinations of molecular descriptors were investigated using a rigorous training-validation procedure. The best SVM classifier can give satisfactory predictions for the training set (97.8% for the poor-absorption class and 94.5% for the good-absorption class). Moreover, 100% of the poor-absorption class and 97.8% of the good-absorption class in the external test set could be correctly classified. Finally, the influence of the size of the training set and the unbalanced nature of the data set have been studied. The analysis demonstrates that large data set is necessary for the stability of the classification models. Furthermore, the weights for the poor-absorption class and the good-absorption class should be properly balanced to generate unbiased classification models. Our work illustrates that SVMs used in combination with simple molecular descriptors can provide an extremely reliable assessment of intestinal absorption in an early in silico filtering process

SLNSpeech: solving extended speech separation problem by the help of sign language

A speech separation task can be roughly divided into audio-only separation and audio-visual separation. In order to make speech separation technology applied in the real scenario of the disabled, this paper presents an extended speech separation problem which refers in particular to sign language assisted speech separation. However, most existing datasets for speech separation are audios and videos which contain audio and/or visual modalities. To address the extended speech separation problem, we introduce a large-scale dataset named Sign Language News Speech (SLNSpeech) dataset in which three modalities of audio, visual, and sign language are coexisted. Then, we design a general deep learning network for the self-supervised learning of three modalities, particularly, using sign language embeddings together with audio or audio-visual information for better solving the speech separation task. Specifically, we use 3D residual convolutional network to extract sign language features and use pretrained VGGNet model to exact visual features. After that, an improved U-Net with skip connections in feature extraction stage is applied for learning the embeddings among the mixed spectrogram transformed from source audios, the sign language features and visual features. Experiments results show that, besides visual modality, sign language modality can also be used alone to supervise speech separation task. Moreover, we also show the effectiveness of sign language assisted speech separation when the visual modality is disturbed. Source code will be released in http://cheertt.top/homepage/Comment: 33 pages, 8 figures, 5 table

Synthesis of ultra-narrow PbTe nanorods with extremely strong quantum confinement

Monodisperse, high-quality, ultra-narrow PbTe nanorods were synthesized for the first time in a one-pot, hot-injection reaction using trans-2-decenoic acid as the agents for lead precursors and tris(diethylamino)phosphine telluride together with free tris(diethylamino)phosphine as the telluride precursors. High monomer reactivity, rapid nucleation and fast growth rate derived from the new precursors led to the anisotropic growth of PbTe nanocrystals at low reaction temperatures

Spontaneous breaking and re-making of the RS-Au-SR staple in self-assembled ethylthiolate/Au(111) interface

The stability of the self-assembled RS–Au–SR (R = CH₂CH₃)/Au­(111) interface at room temperature has been investigated using scanning tunneling microscopy (STM) in conjunction with density functional theory (DFT) and MD calculations. The RS–Au–SR staple, also known as Au-adatom-dithiolate, assembles into staple rows along the [112̅] direction. STM imaging reveals that while the staple rows are able to maintain a static global structure, individual staples within the row are subjected to constant breaking and remaking of the Au–SR bond. The C₂S–Au–SC₂/Au­(111) interface is under a dynamic equilibrium and it is far from rigid. DFT/MD calculations show that a transient RS–Au–Au–SR complex can be formed when a free Au atom is added to the RS–Au–SR staple. The relatively high reactivity of the RS–Au–SR staple at room temperature could explain the reactivity of thiolate-protected Au nanoclusters, such as their ability to participate in ligand exchange and intercluster reactions