An Effective Computational Method for Predicting Self-Interacting Proteins Based on VGGNet Convolutional Neural Network and Gray-Level Co-occurrence Matrix.

Introduction: Predicting Self-interacting proteins (SIPs) is a crucial area of research in predicting protein functions, as well as in understanding gene-disease and disease-drug associations. These interactions are integral to numerous cellular processes and play pivotal roles within cells. However, traditional methods for identifying SIPs through biological experiments are often expensive, time-consuming, and have long cycles.

A mouse model of a novel missense mutation (Gly83Arg) in a Chinese kindred manifesting vitreous amyloidosis only.

The purpose of this study is to establish a mouse model of transthyretin (TTR) Gly83Arg gene mutation by the technique of gene targeting for research on hereditary vitreous amyloidosis (HVA) and to confirm whether this point mutation is a genetic feature of HVA. A vector (pBR322-MK-TTR) was constructed to target ES cells. The successfully transfected ES cells were used for blastocyst injection, thus generating F0.