Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening.

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED's ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC50 value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

G2GT: Retrosynthesis Prediction with Graph-to-Graph Attention Neural Network and Self-Training.

Retrosynthesis prediction, the task of identifying reactant molecules that can be used to synthesize product molecules, is a fundamental challenge in organic chemistry and related fields. To address this challenge, we propose a novel graph-to-graph transformation model, G2GT. The model is built on the standard transformer structure and utilizes graph encoders and decoders. Additionally, we demonstrate the effectiveness of self-training, a data augmentation technique that utilizes unlabeled molecular data, in improving the performance of the model. To further enhance diversity, we propose a weak ensemble method, inspired by reaction-type labels and ensemble learning. This method incorporates beam search, nucleus sampling, and top-k sampling to improve inference diversity. A simple ranking algorithm is employed to retrieve the final top-10 results. We achieved new state-of-the-art results on both the USPTO-50K data set, with a top-1 accuracy of 54%, and the larger more challenging USPTO-Full data set, with a top-1 accuracy of 49.3% and competitive top-10 results. Our model can also be generalized to all other graph-to-graph transformation tasks. Data and code are available at https://github.com/Anonnoname/G2GT_2.

High-flexibility and high-accuracy phase delay calibration method for MEMS-based fringe projection systems.

Microelectromechanical system (MEMS) mirror based laser beam scanning (LBS) projectors for fringe projection profilometry (FPP) are becoming increasingly popular attributing to their small size and low cost. However, the initial phase of the scanning MEMS mirror employed in an LBS projector may vary over time, resulting in unstable and distorted fringe patterns. The distorted fringe patterns will largely decrease the accuracy of the three-dimensional (3D) topographic reconstruction. In this paper, an efficient phase delay calibration method based on a unique fringe projection sequence and a corresponding image processing algorithm is proposed. The proposed method can compensate the phase uncertainty and variation with no need to add any extra components. One LBS projector has been constructed using a uniaxial electrostatic MEMS mirror that has a mirror size of 2.5 mm × 2.5 mm and a scanning field of view of 60 ∘ at its resonance of 1523 Hz. 3D reconstruction experiments are conducted to study how the 3D reconstruction results are affected by the phase delay. The standard deviation of a sphere reconstruction is improved from 2.05 mm to 0.20 mm after the positive phase delay deviation of 5 µs is compensated using this new calibration method.

A pocket-based 3D molecule generative model fueled by experimental electron density.

We report for the first time the use of experimental electron density (ED) as training data for the generation of drug-like three-dimensional molecules based on the structure of a target protein pocket. Similar to a structural biologist building molecules based on their ED, our model functions with two main components: a generative adversarial network (GAN) to generate the ligand ED in the input pocket and an ED interpretation module for molecule generation. The model was tested on three targets: a kinase (hematopoietic progenitor kinase 1), protease (SARS-CoV-2 main protease), and nuclear receptor (vitamin D receptor), and evaluated with a reference dataset composed of over 8000 compounds that have their activities reported in the literature. The evaluation considered the chemical validity, chemical space distribution-based diversity, and similarity with reference active compounds concerning the molecular structure and pocket-binding mode. Our model can generate molecules with similar structures to classical active compounds and novel compounds sharing similar binding modes with active compounds, making it a promising tool for library generation supporting high-throughput virtual screening. The ligand ED generated can also be used to support fragment-based drug design. Our model is available as an online service to academic users via https://edmg.stonewise.cn/#/create .

Observing Noncovalent Interactions in Experimental Electron Density for Macromolecular Systems: A Novel Perspective for Protein-Ligand Interaction Research.

We report for the first time the use of experimental electron density (ED) in the Protein Data Bank for modeling of noncovalent interactions (NCIs) for protein-ligand complexes. Our methodology is based on reduced electron density gradient (RDG) theory describing intermolecular NCIs by ED and its first derivative. We established a database named Experimental NCI Database (ExptNCI; http://ncidatabase.stonewise.cn/#/nci) containing ED saddle points, indicating ∼200,000 NCIs from over 12,000 protein-ligand complexes. We also demonstrated the usage of the database in the case of depicting amide-π interactions in protein-ligand binding systems. In summary, the database provides details on experimentally observed NCIs for protein-ligand complexes and can support future studies including studies on rarely documented NCIs and the development of artificial intelligence models for protein-ligand binding prediction.

Knockdown of long non-coding RNA CCAT2 suppresses growth and metastasis of esophageal squamous cell carcinoma by inhibiting the ß-catenin/WISP1 signaling pathway.

OBJECTIVE: Long non-coding RNA (lncRNA) colon cancer-associated transcript 2 (CCAT2) plays oncogenic roles in several cancers, including esophageal squamous cell carcinoma (ESCC). However, the specific mechanism of how CCAT2 influences ESCC tumorigenesis is still unknown. METHODS: Using RT-qPCR, the mRNA expression levels of CCAT2 in 33 paired ESCC and adjacent non-cancer tissues and cell lines were measured. Lentiviral vector sh-CCAT2 was designed and transfected into TE10 cells. CCK-8 and transwell assays were employed to detect the effects of CCAT2 knockdown on cell proliferation and invasion, respectively. RT-qPCR and western blots were used to detect the effects of CCAT2 knockdown. RESULTS: CCAT2 was overexpressed in ESCC tissues compared with corresponding adjacent tissues. CCAT2 knockdown could suppress cell proliferation and invasion in vitro. Furthermore, knockdown of CCAT2 could suppress the mRNA and protein levels of ß-catenin and Wnt-induced-secreted-protein-1 (WISP1), as well as the mRNA levels of their downstream targets VEGF-A, MMP2, and ICAM-1. High expression of CCAT2 and WISP1 were associated with poor prognosis of ESCC patients. CONCLUSIONS: In conclusion, a novel CCAT2/ß-catenin/WISP1 axis was revealed in ESCC progression and may provide a promising therapeutic target against ESCC. CCAT2 and WISP1 are potential molecular biomarkers for predicting prognosis of ESCC.

Parallel PWMs based fully digital transmitter with wide carrier frequency range.

The carrier-frequency (CF) and intermediate-frequency (IF) pulse-width modulators (PWMs) based on delay lines are proposed, where baseband signals are conveyed by both positions and pulse widths or densities of the carrier clock. By combining IF-PWM and precorrected CF-PWM, a fully digital transmitter with unit-delay autocalibration is implemented in 180 nm CMOS for high reconfiguration. The proposed architecture achieves wide CF range of 2 M-1 GHz, high power efficiency of 70%, and low error vector magnitude (EVM) of 3%, with spectrum purity of 20 dB optimized in comparison to the existing designs.