ProteinF3S: boosting enzyme function prediction by fusing protein sequence, structure, and surface.

Proteins can be represented in different data forms, including sequence, structure, and surface, each of which has unique advantages and certain limitations. It is promising to fuse the complementary information among them. In this work, we propose a framework called ProteinF3S for enzyme function prediction that fuses the complementary information across protein sequence, structure, and surface.

PGBind: pocket-guided explicit attention learning for protein-ligand docking.

As more and more protein structures are discovered, blind protein-ligand docking will play an important role in drug discovery because it can predict protein-ligand complex conformation without pocket information on the target proteins. Recently, deep learning-based methods have made significant advancements in blind protein-ligand docking, but their protein features are suboptimal because they do not fully consider the difference between potential pocket regions and non-pocket regions in protein feature extraction. In this work, we propose a pocket-guided strategy for guiding the ligand to dock to potential docking regions on a protein.

Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction.

Self-supervised learning plays an important role in molecular representation learning because labeled molecular data are usually limited in many tasks, such as chemical property prediction and virtual screening. However, most existing molecular pre-training methods focus on one modality of molecular data, and the complementary information of two important modalities, SMILES and graph, is not fully explored. In this study, we propose an effective multi-modality self-supervised learning framework for molecular SMILES and graph.

ProteinMAE: masked autoencoder for protein surface self-supervised learning.

Summary: The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achieved inspirational performance in many tasks such as protein design, protein-protein interaction prediction, etc. However, they are still limited by the problem of label scarcity, since the labels are typically obtained through wet experiments.

Pressure-induced structural and electronic transitions of thiospinel FeS.

We report the investigations on the structural and electronic properties of an inverse spinel FeS at high pressures using synchrotron x-ray diffraction (XRD) and electrical transport measurements. Our XRD measurements at high pressures reveal an irreversible structural phase transformation on compression above  ∼3 GPa from a cubic spinel (Fd-3m space group) into a monoclinic CrS-type structure (I2/m space group). Electrical transport measurements suggest that the high pressure monoclinic phase has a semiconducting behavior.

Application of apparent diffusion coefficient and exponent apparent diffusion coefficient values in magnetic resonance imaging diffusion-weighted imaging to differentiate benign and malignant ovarian epithelial tumors.

Objective: The aim of this study was to investigate the value of two quantitative indicators, the apparent diffusion coefficient (ADC) and the exponent apparent diffusion coefficient (EADC), of magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in the differential diagnosis of ovarian epithelial tumors.

Materials And Methods: Clinical and MRI data from ovarian epithelial tumors were analyzed after pathology confirmation of 85 lesions from 76 cases (47 lesions from 41 benign cases; 38 lesions from 35 malignant cases). Patients underwent routine MRI examination and DWI before surgery.