Motivation Protein-protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately both physical modeling and machine learning methods suffer from poor precision and/or recall. Results In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, “PInet” (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition. Availability PInet scripts and models are available at https://github.com/FTD007/PInet. Supplementary information Supplementary data are available at Bioinformatics online.

Carbon aerogels are among the most attractive porous carbon materials currently, but their real-world applications are greatly limited by their high cost, complicated preparation process and low mechanical properties. Herein, we report a very facile route to prepare lightweight but mechanically strong carbon aerogel monoliths (CAMs), through a sol-gel polymerization of linear phenolic resin and hexamethylenetetramine (HMTA), followed by ambient pressure drying and carbonization. The good capability of linear phenolic resin with ethanol could induce the formation of large polymer particle and good particle connectivity, affording robust network to suppress the collapse during the ambient drying. The synthesis is scalable and flexible, permitting a facile tailor of density, porous structure and mechanical strength by adjusting the ratio of phenolic resin to HMTA and phenolic resin concentration. The obtained CAMs possess macroporous/microporous hierarchical structure with low density as low as 0.07 g cm -3 , high mechanical strength of 0.9-5.0 MPa and low thermal conductivity (0.032-0.069 W m -l K -1 ). Further CO 2 activation can greatly develop the microporosity without sacrificing the monolithic structure. Moreover, as-prepared CAMs can be fabricated in large sizes, as well as being post-machined into many shapes and sizes for potential applications.

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The metallurgical properties and the microstructure of coke after gasification reaction with pure H2O and pure CO2 were investigated in this study. Moreover, the first-principles calculation was conducted to study the reaction process of the carbon with pure H2O and pure CO2. The results show that the CRI (coke reaction index) increases sharply and the CSR (coke strength after reaction) decreases sharply, when the cokes are gasified with H2O as compared to CO2. The scanning electronic microscopy images and the coke panoramagrams show that H2O more easily leads to the generation of large pores (>500 μm) and destroys the coke structure than CO2. The X-ray diffraction results indicate that the arrangement of carbon atoms of coke becomes regular and the ordered degree of coke increases after reaction with CO2 and H2O; however, after being gasified with H2O, the cokes have a higher ordered degree than with CO2. The results of the first-principles calculation show that the H2O molecule is more likely to react with carbon as compared to the CO2 molecule due to the lower energy barriers of H2O adsorption and H2 formation. The M2 → FS reaction process is the controlled step of the C-H2O reaction process, as well as in the C-CO2 reaction system.