Saadat, M. | bioRxiv Science

bioRxiv2022

The effects of oral supplementation of Japanese sake yeast on anxiety, depressive-like symptoms, oxidative stress, and BDNF changes in chronically stressed adolescent rats

Chronic stress during the pre-pubertal period has adverse effects in developing neuropsychiatric disorders such as depression and anxiety. The administration of supplements with antioxidant properties may alleviate depression and anxiety behavior. This study investigated the effects of oral supplementation of Japanese sake yeast on anxiety, depressive-like symptoms, oxidative stress, and changes in brain-derived neurotropic factor (BDNF) in adolescence rats subjected to chronic stress. In order to assess the effects of chronic stress, adolescent rats were grouped into one non-stressed control group (non-stress) and four different experimental groups. The other animals were subjected to stress and orally received normal saline (Control-stress), 15 mg/kg yeast (Stress-15), 30 mg/kg yeast (Stress-30) and 45 mg/kg yeast (Stress-45). Anxiety and depression-like behavior, BDNF levels, and oxidative stress markers were evaluated. The rats exposed to stress exhibited anxiogenic and depression-like behavior as well as lower levels of BDNF and higher levels of oxidative markers compared with non-stressed rats (P<0.05). However, the oral supplementation of sake yeast decreased anxiogenic and depression-like behavior and oxidative indices, and also increased BDNF levels compared to stressed rats treated with saline in a dose-dependent manner (P<0.05). In sum, stress caused anxiety and depression behavior, increased oxidative indices, and reduced BDNF levels while sake yeast alleviated adverse effects of stress on anxiety and depression behaviors, decreased oxidative markers, and increased BDNF levels.

neuroscience↗

bioRxiv2021

TranDTA: Prediction Of Drug Target Binding Affinity Using Transformer Representations

Drug discovery is generally difficult, expensive, and low success rate. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug-target interactions. Binding affinity indicates the strength of drug-target pair interactions. In this regard, several computational methods have been developed to predict the drug-target binding affinity, and the input representation of these models has been shown to be very effective in improving accuracy. Although the recent models predict binding affinity more accurate than the first ones, they need the structure of target proteins. Despite the strong interest in protein structure, there is a massive gap between known sequences and experimentally determined structures. Therefore, finding an appropriate presentation for drug and protein sequences is vital for drug-target binding affinity prediction. In this paper, our primary goal is to assess the drug and protein sequence representation for improving drug-target binding affinity prediction.

bioinformatics↗