Optimization of Protein-Protein Interaction Measurements for Drug Discovery Using AFM Force Spectroscopy

Increasingly targeted in drug discovery, protein-protein interactions challenge current high throughput screening technologies in the pharmaceutical industry. Developing an effective and efficient method for screening small molecules or compounds is critical to accelerate the discovery of ligands for enzymes, receptors and other pharmaceutical targets. Here, we report developments of methods to increase the signal-to-noise ratio (SNR) for screening protein-protein interactions using atomic force microscopy (AFM) force spectroscopy. We have demonstrated the effectiveness of these developments on detecting the binding process between focal adhesion kinases (FAK) with protein kinase B (Akt1), which is a target for potential cancer drugs. These developments include optimized probe and substrate functionalization processes and redesigned probe-substrate contact regimes. Furthermore, a statistical-based data processing method was developed to enhance the contrast of the experimental data. Collectively, these results demonstrate the potential of the AFM force spectroscopy in automating drug screening with high throughput

Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy

Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields.Comment: 13 pages,7 figure

Biochar has no effect on soil respiration across Chinese agricultural soils

This work was supported by NSFC (41371298 and 41371300), Ministry of Science and Technology (2013GB23600666 and 2013BAD11B00), and Ministry of Education of China (20120097130003). The international cooperation was funded under a “111” project by the State Agency of Foreign Expert Affairs of China and jointly supported under a grant for Priority Disciplines in Higher Education by the Department of Education, Jiangsu Province, China; The work was also a contribution to the cooperation project of “Estimates of Future Agricultural GHG Emissions and Mitigation in China” under the UK-China Sustainable Agriculture Innovation Network (SAIN). Pete Smith contributed to this work under a UK BBSRC China Partnership Award. The authors are grateful to Yuming Liu, Bin Zhang, Xiao Li, Gang Wu, Jinjin Qu and Yinxin Ye and Dongqi Liu for their contribution to field experiments, and to Rongjun Bian and Qaiser Hussain for their participation in discussions of the data analysis and interpretation, and to Xinyan Yu and Jiafang Wang for their assistance in lab works.Peer reviewedPostprin

Biochar-based fertilizer: Supercharging root membrane potential and biomass yield of rice

Biochar-based compound fertilizers (BCF) and amendments have proven to enhance crop yields and modify soil properties (pH, nutrients, organic matter, structure etc.) and are now in commercial production in China. While there is a good understanding of the changes in soil properties following biochar addition, the interactions within the rhizosphere remain largely unstudied, with benefits to yield observed beyond the changes in soil properties alone. We investigated the rhizosphere interactions following the addition of an activated wheat straw BCF at an application rates of 0.25% (g·g−1 soil), which could potentially explain the increase of plant biomass (by 67%), herbage N (by 40%) and P (by 46%) uptake in the rice plants grown in the BCF-treated soil, compared to the rice plants grown in the soil with conventional fertilizer alone. Examination of the roots revealed that micron and submicron-sized biochar were embedded in the plaque layer. BCF increased soil Eh by 85 mV and increased the potential difference between the rhizosphere soil and the root membrane by 65 mV. This increased potential difference lowered the free energy required for root nutrient accumulation, potentially explaining greater plant nutrient content and biomass. We also demonstrate an increased abundance of plant-growth promoting bacteria and fungi in the rhizosphere. We suggest that the redox properties of the biochar cause major changes in electron status of rhizosphere soils that drive the observed agronomic benefits