Waugh Nymand (supplyplain50)

Clathrin is a highly evolutionarily conserved protein, which can affect membrane cleavage and membrane release of vesicles. The absence of clathrin in the cellular system affects a variety of human diseases. Effective recognition of clathrin plays an important role in the development of drugs to treat related diseases. In recent years, deep learning has been widely applied in the field of bioinformatics because of its high efficiency and accuracy. In this study, we propose a deep learning framework, DeepCLA, which combines two different network structures, including a convolutional neural network and a bidirectional long short-term memory network to identify clathrin. The investigation of different deep network architectures demonstrates that the prediction performance of a hybrid depth network model is better than that of a single depth network. On the independent test dataset, DeepCLA outperforms the state-of-the-art methods. It suggests that DeepCLA is an effective approach for clathrin prediction and can provide more instructive guidance for further experimental investigation of clathrin. Moreover, the source code and training data of DeepCLA are provided at https//github.com/ZhangZhang89/DeepCLA.We report plasmon-free polymeric nanowrinkled substrates for surface-enhanced Raman spectroscopy (SERS). selleck kinase inhibitor Our simple, rapid, and cost-effective fabrication method involves depositing a poly(ethylene glycol)diacrylate (PEGDA) prepolymer solution droplet on a fully polymerized, flat PEGDA substrate, followed by drying the droplet at room conditions and plasma treatment, which polymerizes the deposited layer. The thin polymer layer buckles under axial stress during plasma treatment due to its different mechanical properties from the underlying soft substrate, creating hierarchical wrinkled patterns. We demonstrate the variation of the wrinkling wavelength with the drying polymer molecular weight and concentration (direct relations are observed). A transition between micron to nanosized wrinkles is observed at 5 v % concentration of the lower molecular-weight polymer solution (PEGDA Mn 250). The wrinkled substrates are observed to be reproducible, stable (at room conditions), and, especially, homogeneous at and below the transition regime, where nanowrinkles dominate, making them suitable candidates for SERS. As a proof-of-concept, the enhanced SERS performance of micro/nanowrinkled surfaces in detecting graphene and hexagonal boron nitride (h-BN) is illustrated. Compared to the SiO2/Si surfaces, the wrinkled PEGDA substrates significantly enhanced the signature Raman band intensities of graphene and h-BN by a factor of 8 and 50, respectively.Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, focuses on accuracy but leaves much to be desired for interpretability. Defining intermolecular contacts underlying affinities as a vehicle for interpretability; our large-scale interpretability assessment finds previously used attention mechanisms inadequate. We thus formulate a hierarchical multiobjective learning problem, where predicted contacts form the basis for predicted affinities. We solve the problem by embedding protein sequences (by hierarchical recurrent neural networks) and compound graphs (by graph neural networks) with joint attentions between protein residues and compound atoms. We further introduce three methodological advances to enhance interpretability (1) structure-aware regularization of attentions using protein sequence-predicted solvent expdel assessment dedicated to interpretable machine learning for structure-free compound-protein affinity prediction.The field confinement of plasmonic systems enables spectral tunability under structural variations or environmental perturbations, which is the princi