As the endpoint for the ubiquitin-proteasome system, the 26S proteasome is the principal proteolytic machine responsible for regulated protein degradation in eukaryotic cells. The proteasome's cellular functions range from general protein homeostasis and stress response to the control of vital processes such as cell division and signal transduction. To reliably process all the proteins presented to it in the complex cellular environment, the proteasome must combine high promiscuity with exceptional substrate selectivity. Recent structural and biochemical studies have shed new light on the many steps involved in proteasomal substrate processing, including recognition, deubiquitination, and ATP-driven translocation and unfolding. In addition, these studies revealed a complex conformational landscape that ensures proper substrate selection before the proteasome commits to processive degradation. These advances in our understanding of the proteasome's intricate machinery set the stage for future studies on how the proteasome functions as a major regulator of the eukaryotic proteome.

In this study, we address the challenge of consistently following emotional support strategies in long conversations by large language models (LLMs). We introduce the Strategy-Relevant Attention (SRA) metric, a model-agnostic measure designed to evaluate the effectiveness of LLMs in adhering to strategic prompts in emotional support contexts. By analyzing conversations within the Emotional Support Conversations dataset (ESConv) using LLaMA models, we demonstrate that SRA is significantly correlated with a model's ability to sustain the outlined strategy throughout the interactions. Our findings reveal that the application of SRAinformed prompts leads to enhanced strategic adherence, resulting in conversations that more reliably exhibit the desired emotional support strategies over longer conversations. Furthermore, we contribute a comprehensive, multibranch synthetic conversation dataset for ES-Conv, featuring a variety of strategy continuations informed by our optimized prompting method. The code and data are publicly available on our Github. 1

The majority of biological turnover of lignocellulosic biomass in nature is conducted by fungi, which commonly use Family 1 carbohydrate-binding modules (CBMs) for targeting enzymes to cellulose. Family 1 CBMs are glycosylated, but the effects of glycosylation on CBM function remain unknown. Here, the effects of O-mannosylation are examined on the Family 1 CBM from the Trichoderma reesei Family 7 cellobiohydrolase at three glycosylation sites. To enable this work, a procedure to synthesize glycosylated Family 1 CBMs was developed. Subsequently, a library of 20 CBMs was synthesized with mono-, di-, or trisaccharides at each site for comparison of binding affinity, proteolytic stability, and thermostability. The results show that, although CBM mannosylation does not induce major conformational changes, it can increase the thermolysin cleavage resistance up to 50-fold depending on the number of mannose units on the CBM and the attachment site. O-Mannosylation also increases the thermostability of CBM glycoforms up to 16°C, and a mannose disaccharide at Ser3 seems to have the largest themostabilizing effect. Interestingly, the glycoforms with small glycans at each site displayed higher binding affinities for crystalline cellulose, and the glycoform with a single mannose at each of three positions conferred the highest affinity enhancement of 7.4-fold. Overall, by combining chemical glycoprotein synthesis and functional studies, we show that specific glycosylation events confer multiple beneficial properties on Family 1 CBMs. chemical synthesis | cellulase | biofuels | protein engineering

The importance of the glycan structure and size, amino acid residues near the glycosylation site, and glycosidic linkage in controlling the effects of CBM O-glycosylation is shown.