E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery.

Structural analogue-based protein structure domain assembly assisted by deep learning.

Motivation: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

Motivation: Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy.

Association between well-characterized gene polymorphisms and the hypnosis response caused by sevoflurane-induced anaesthesia.

What Is Known And Objective: Sevoflurane is the most widely used volatile anaesthetic in clinical practice. It exhibits a hypnotic (unconsciousness) effect and causes a loss of reaction to noxious stimuli (immobility). However, to date, the mechanism of action of sevoflurane is poorly understood.