Brain organoids derived from human pluripotent stem cells provide a highly valuable in vitro model to recapitulate human brain development and neurological diseases. However, the current systems for ...brain organoid culture require further improvement for the reliable production of high-quality organoids. Here, we demonstrate two engineering elements to improve human brain organoid culture, (1) a human brain extracellular matrix to provide brain-specific cues and (2) a microfluidic device with periodic flow to improve the survival and reduce the variability of organoids. A three-dimensional culture modified with brain extracellular matrix significantly enhanced neurogenesis in developing brain organoids from human induced pluripotent stem cells. Cortical layer development, volumetric augmentation, and electrophysiological function of human brain organoids were further improved in a reproducible manner by dynamic culture in microfluidic chamber devices. Our engineering concept of reconstituting brain-mimetic microenvironments facilitates the development of a reliable culture platform for brain organoids, enabling effective modeling and drug development for human brain diseases.
Charge redistribution on surface of Ru nanoparticle can significantly affect electrocatalytic HER activity. Herein, a double atomic‐tuned RuBi SAA/Bi@OG nanostructure that features RuBi single‐atom ...alloy nanoparticle supported by Bi−O single‐site‐doped graphene was successfully developed by one‐step pyrolysis method. The alloyed Bi single atom and adjacent Bi−O single site in RuBi SAA/Bi@OG can synergistically manipulate electron transfer on Ru surface leading to optimum charge redistribution. Thus, the resulting RuBi SAA/Bi@OG exhibits superior alkaline HER activity. Its mass activity is up to 65000 mA mg−1 at an overpotential of 150 mV, which is 72.2 times as much as that of commercial Pt/C. DFT calculations reveal that the RuBi SAA/Bi@OG possesses the optimum charge redistribution, which is most beneficial to strengthen adsorption of water and weaken hydrogen‐adsorption free energy in HER process. This double atomic‐tuned strategy on surface charge redistribution of Ru nanoparticle opens a new way to develop highly efficient electrocatalysts.
A double atomic‐tuned RuBi SAA/Bi@OG nanostructure was prepared by one‐step pyrolysis method. The electron density on surface of Ru nanoparticle can be synergistically modulated by alloyed Bi single atom and adjacent Bi−O single site leading to optimum charge redistribution. Thus, the resulting RuBi SAA/Bi@OG exhibits superior alkaline HER activity.
Abstract
Motivation
Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, ...which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank.
Results
We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets.
Availability and implementation
https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE.
Supplementary information
Supplementary data are available at Bioinformatics online.
We report the results of residue‐residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline ...starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)‐based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact‐map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end‐to‐end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free‐modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long‐range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.
Developing high‐energy‐density electrodes for lithium ion batteries (LIBs) is of primary importance to meet the challenges in electronics and automobile industries in the near future. Conversion ...reaction‐based transition metal oxides are attractive candidates for LIB anodes because of their high theoretical capacities. This review summarizes recent advances on the development of nanostructured transition metal oxides for use in lithium ion battery anodes based on conversion reactions. The oxide materials covered in this review include oxides of iron, manganese, cobalt, copper, nickel, molybdenum, zinc, ruthenium, chromium, and tungsten, and mixed metal oxides. Various kinds of nanostructured materials including nanowires, nanosheets, hollow structures, porous structures, and oxide/carbon nanocomposites are discussed in terms of their LIB anode applications.
Conversion reaction‐based oxides are considered promising anode materials to replace graphite due to their high theoretical capacity. This review summarizes recent advances in the development of nanostructured transition metal oxides for use in lithium ion battery anodes based on conversion reactions. Moreover, some important aspects and future directions for designing high‐performance anodes are discussed.
Tuning the side chains of conjugated polymers is a simple, yet effective strategy for modulating their structural and electrical properties, but their impact on n‐type conjugated polymers has not ...been studied extensively, particularly in the area of all‐polymer solar cells (all‐PSCs). Herein, the effects of side chain engineering of P(NDI2OD‐T2) polymer (also known as Polyera Activink N2200) are investigated, which is the most widely used n‐type polymer in all‐PSCs and organic field‐effect transistors (OFETs), on their structural and electronic properties. A series of naphthalenediimide‐bithiophene‐based copolymers (P(NDIR‐T2)) is synthesized, with different side chains (R) of 2‐hexyldecyl (2‐HD), 2‐octyldodecyl (2‐OD), and 2‐decyltetradecyl (2‐DT). The P(NDI2HD‐T2) exhibits more noticeable crystalline behaviors than P(NDI2OD‐T2) and P(NDI2DT‐T2), thereby facilitating superior 3D charge transport. For example, the P(NDI2HD‐T2) shows the highest OFET electron mobility (1.90 cm2 V−1 s−1). Also, a series of all‐PSCs is produced using different electron donors of PTB7‐Th, PTB7, and PPDT2FBT. The P(NDI2HD‐T2) based all‐PSCs produce much higher power conversion efficiency (PCE) irrespective of the electron donors. In particular, the PTB7‐Th:P(NDI2HD‐T2) forms highly ordered, strong face‐on interchain stackings, and has better intermixed bulk‐heterojunction morphology, producing the highest PCE of 6.11% that has been obtained by P(NDIR‐T2) based all‐PSCs to date.
The effect of side chain modification of naphthalenediimide–bithiophene (NDI‐T2)‐based n‐type polymers on their structural and electronic properties has been investigated. The P(NDI2HD‐T2)‐based all‐polymer solar cells produce much better performance than other NDI‐T2‐based devices (i.e., P(NDI2OD‐T2) (Polyera Activink N2200)) irrespective of the electron donor due to superb electron transport ability of P(NDI2HD‐T2).
Protein–ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein ...function annotation and drug discovery. However, there is no method that can optimally identify ATP binding sites for different proteins. In this study, we report a new composite predictor, ATPbind, for ATP binding sites by integrating the outputs of two template-based predictors (i.e., S-SITE and TM-SITE) and three discriminative sequence-driven features of proteins: position specific scoring matrix, predicted secondary structure, and predicted solvent accessibility. In ATPbind, we assembled multiple support vector machines (SVMs) based on a random undersampling technique to cope with the serious imbalance phenomenon between the numbers of ATP binding sites and of non-ATP binding sites. We also constructed a new gold-standard benchmark data set consisting of 429 ATP binding proteins from the PDB database to evaluate and compare the proposed ATPbind with other existing predictors. Starting from a query sequence and predicted I-TASSER models, ATPbind can achieve an average accuracy of 72%, covering 62% of all ATP binding sites while achieving a Matthews correlation coefficient value that is significantly higher than that of other state-of-the-art predictors.
Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising target for the discovery of specific inhibitors as antitumor drugs. Based on the source of compounds, all LSD1 inhibitors in this ...review are divided into two categories: natural LSD1 inhibitors and synthetic LSD1 inhibitors. This review highlights the research progress of LSD1 inhibitors with the potential to treat cancer covering articles published in 2020. Design strategies, structure-activity relationships, co-crystal structure analysis and action mechanisms are also highlighted.
Display omitted
•LSD1 is a promising therapeutic target for cancer therapy.•LSD1 inhibitors published in 2020 are highlighted.•Co-crystal structures of representative LSD1/ inhibitors were highlighted.
Abstract
Motivation
Sequence-order independent structural comparison, also called structural alignment, of small ligand molecules is often needed for computer-aided virtual drug screening. Although ...many ligand structure alignment programs are proposed, most of them build the alignments based on rigid-body shape comparison which cannot provide atom-specific alignment information nor allow structural variation; both abilities are critical to efficient high-throughput virtual screening.
Results
We propose a novel ligand comparison algorithm, LS-align, to generate fast and accurate atom-level structural alignments of ligand molecules, through an iterative heuristic search of the target function that combines inter-atom distance with mass and chemical bond comparisons. LS-align contains two modules of Rigid-LS-align and Flexi-LS-align, designed for rigid-body and flexible alignments, respectively, where a ligand-size independent, statistics-based scoring function is developed to evaluate the similarity of ligand molecules relative to random ligand pairs. Large-scale benchmark tests are performed on prioritizing chemical ligands of 102 protein targets involving 1 415 871 candidate compounds from the DUD-E (Database of Useful Decoys: Enhanced) database, where LS-align achieves an average enrichment factor (EF) of 22.0 at the 1% cutoff and the AUC score of 0.75, which are significantly higher than other state-of-the-art methods. Detailed data analyses show that the advanced performance is mainly attributed to the design of the target function that combines structural and chemical information to enhance the sensitivity of recognizing subtle difference of ligand molecules and the introduces of structural flexibility that help capture the conformational changes induced by the ligand-receptor binding interactions. These data demonstrate a new avenue to improve the virtual screening efficiency through the development of sensitive ligand structural alignments.
Availability and implementation
http://zhanglab.ccmb.med.umich.edu/LS-align/
Supplementary information
Supplementary data are available at Bioinformatics online.
•A novel, hybrid artificial neural network – EMOS postprocessing scheme is proposed.•The hybrid scheme allows for simultaneous postprocessing of precipitation forecasts for multiple seasons and lead ...times.•The scheme outperforms existing EMOS schemes as judged by the skills of postprocessed probabilistic precipitation forecasts.
Many present-day statistical schemes for postprocessing weather forecasts, in particular precipitation forecasts, rely on calibration using prescribed statistical models to relate forecast statistics to distributional parameters. The efficacy of such schemes is often constrained not only by prescribed predictor-predictand relation, but also by arbitrary choices of temporal window and lead time range for training. To address this limitation, we propose an end-to-end, computationally efficient hybrid postprocessing scheme capable of producing full predictive distributions of precipitation accumulation without explicit stratification of forecast-observation pairs by forecast lead time and season. The proposed framework uses the censored, shifted gamma distribution (CSGD) as the predictive distribution but uses an artificial neural network (ANN) to estimate the distributional parameters of CSGD through a unified approach. This approach, referred to as ANN-CSGD, allows for simultaneous estimation of distributional parameters over multiple lead times and seasons in a single model by incorporating the latter variables as predictors to the ANN. We test our proposed ANN-CSGD model for postprocessing of ensemble mean forecasts of 24-h precipitation totals over selected river basins in California, at one- to seven-day lead times, from the Global Ensemble Forecast System (GEFS). The probabilistic quantitative precipitation forecasts (PQPFs) from the ANN-CSGD, are more skillful overall than those from the benchmark CSGD and the Mixed-type meta-Gaussian distribution (MMGD) models. The ANN-CSGD PQPFs highly improve the performance of those from CSGD in predicting the probability of precipitation (PoP) and are also much sharper and reliable at higher precipitation thresholds. We demonstrate how the hybrid approach, by using the entire available training data and its modified formulation, efficiently represents interactions between GEFS forecasts and season/lead times, thus leading to enhanced predictive performance.
PDF
