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Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the application of deep convolutional and graph neural network-based approaches, it remains unclear what the relative advantages of each approach are and how they compare with physics-based methodologies that have found more mainstream success in virtual screening pipelines. We present fusion models that combine features and inference from complementary representations to improve binding affinity prediction. This, to our knowledge, is the first comprehensive study that uses a common series of evaluations to directly compare the performance of three-dimensional (3D)-convolutional neural networks (3D-CNNs), spatial graph neural networks (SG-CNNs), and their fusion. We use temporal and structure-based splits to assess performance on novel protein targets. To test the practical applicability of our models, we examine their performance in cases that assume that the crystal structure is not available. In these cases, binding free energies are predicted using docking pose coordinates as the inputs to each model. In addition, we compare these deep learning approaches to predictions based on docking scores and molecular mechanic/generalized Born surface area (MM/GBSA) calculations. Our results show that the fusion models make more accurate predictions than their constituent neural network models as well as docking scoring and MM/GBSA rescoring, with the benefit of greater computational efficiency than the MM/GBSA method. Finally, we provide the code to reproduce our results and the parameter files of the trained models used in this work. The software is available as open source at https//github.com/llnl/fast. Model parameter files are available at ftp//gdo-bioinformatics.ucllnl.org/fast/pdbbind2016_model_checkpoints/.Sodium niobate (NaNbO3) attracts attention for its great potential in a variety of applications, for instance, due to its unique optical properties. Still, optimization of its synthetic procedures is hard due to the lack of understanding of the formation mechanism under hydrothermal conditions. Through in situ X-ray diffraction, hydrothermal synthesis of NaNbO3 was observed in real time, enabling the investigation of the reaction kinetics and mechanisms with respect to temperature and NaOH concentration and the resulting effect on the product crystallite size and structure. Several intermediate phases were observed, and the relationship between them, depending on temperature, time, and NaOH concentration, was established. learn more The reaction mechanism involved a gradual change of the local structure of the solid Nb2O5 precursor upon suspending it in NaOH solutions. Heating gave a full transformation of the precursor to HNa7Nb6O19·15H2O, which destabilized before new polyoxoniobates appeared, whose structure depended on the NaOH concentration. Following these polyoxoniobates, Na2Nb2O6·H2O formed, which dehydrated at temperatures ≥285 °C, before converting to the final phase, NaNbO3. The total reaction rate increased with decreasing NaOH concentration and increasing temperature. Two distinctly different growth regimes for NaNbO3 were observed, depending on the observed phase evolution, for temperatures below and above ≈285 °C. Below this temperature, the growth of NaNbO3 was independent of the reaction temperature and the NaOH concentration, while for temperatures ≥285 °C, the temperature-dependent crystallite size showed the characteristics of a typical dissolution-precipitation mechanism.Lysophospholipids are bioactive signaling molecules derived from cell membrane glycerophospholipids or sphingolipids and are highly regulated under normal physiological conditions. Lysophosphatidic acids (LPAs) are a class of lysophospholipids that act on G-protein-coupled receptors to exert a variety of cellular functions. Dysregulation of phospholipase activity and consequently LPA synthesis in serum have been linked to inflammation, such as seen in chronic obstructive pulmonary disease (COPD). The accurate measurement of phospholipids is critical for evaluating their dysregulation in disease. In this study, we optimized experimental parameters for the sensitive measurement of LPAs. We validated the method based on matrix, linearity, accuracy, precision, and stability. An investigation into sample extraction processes emphasized that the common practice of including low concentration of hydrochloric acid in the extraction buffer causes an overestimation of lipid recovery. The liquid chromatography gradient was optimized to separate various lysophospholipid classes. After optimization, detection limits of LPA were sufficiently sensitive for subsequent analysis, ranging from 2 to 8 nM. The validated workflow was applied to a cohort of healthy donor and COPD patient sera. Eight LPA species were identified, and five unique species of LPA were quantified. Most LPA species increased significantly in COPD patients compared to healthy donors. The correlation between LPAs and other demographic parameters was further investigated in a sample set of over 200 baseline patient sera from a COPD clinical trial. For the first time, LPAs other than the two most abundant and readily detectable moieties are quantified in COPD patients using validated methods, opening the door to downstream biomarker evaluation in respiratory disease.Collagen proteins are spread in almost every vertebrate's tissue with mechanical function. The defining feature of this fundamental family of proteins is its well-known collagen triple-helical domain. This helical domain can have different geometries, varying in helical elongation and interstrands contact, as a function of the amino acidic composition. The helical geometrical features play an important role in the interaction of the collagen protein with cell receptors, but for the vast majority of collagen compositions, these geometrical features are unknown. Quantum mechanical (QM) simulations based on the density functional theory (DFT) provide a robust approach to characterize the scenario on the collagen composition-structure relationships. In this work, we analyze the role of the adopted computational method in predicting the collagen structure for two purposes. First, we look for a cost-effective computational approach to apply to a large-scale composition-structure analysis. Second, we attempt to assess the robustness of the predictions by varying the QM methods.
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