bluuebunny/crossref_metadata_2025_split huggingface dataset:
The activation of ATR during unperturbed DNA replication is restricted by VCP/p97 through the extraction of DNA polymerase α/Primase from chromatin
Authors: Rodríguez-Acebes Sara , Martín-Rufo Rodrigo , Gómez-Moya Alicia , Churcher Scott B , Fernández-Llorente Alejandro , de la Vega-Barranco Guillermo , Perona Alejandra , Oroz Pilar , Martín-Doncel Elena , Toledo Luis Ignacio , Méndez Juan , Lecona Emilio 
Published: July 2022

ABSTRACTThe replication stress response is an essential pathway that deals with the obstacles that halt the progression of DNA replication forks even during an unperturbed S phase. Basal activation of the ATR and CHK1 kinases prevents the premature firing of origins of replication during S phase, avoiding the activation of an excessive number of replication forks and the appearance of genomic instability. However, the mechanisms that regulate ATR activation in the unperturbed S phase have not been fully determined. Here we present evidence that the AAA ATPase VCP/p97 regulates the presence of the DNA polymerase α/Primase complex (POLA/PRIM) on chromatin, thus limiting its activity and hampering the subsequent activation of ATR by TOPBP1. As a consequence, inhibiting VCP/p97 activates ATR and CHK1 and leads to a cell cycle arrest in G2/M. We propose that the priming activity of POLA/PRIM in the lagging strand is one of the determinants of the basal activation of ATR during an unperturbed S phase and VCP/p97 limits this activation through the extraction of POLA/PRIM from chromatin.
The response mechanism of oreorchis patens (Lindl.) Lindl. to cold stress
Authors: Yu Lan , Xu Yufeng , Zhang Yuyan , Shao Meini , Miao Qing , Chen Xuhui , Yang Huixia , Cui Na , Qu Bo 
Published: July 2022

AbstractCold stress, a major environmental factor, has an important impact on the production of landscape plants and crop yield, and its stress and resistance mechanisms have always been hot research issues. Oreorchis patens (Lindl.) Lindl., an important germplasm resource, has strong frost resistance and can resist low temperatures of -40 °C. However, the mechanism by which O. patens responds to cold stress remains poorly understood. Here, we examined the adaptation to the low-temperature environment of O. patens by changing the leaf tissue structure, while the synergistic effect of osmotic regulation, reactive oxygen scavenging and protein improved the resistance to cold stress. In addition, analysis of the photosynthetic system showed that cold stress changes the photosynthetic capacity of O. patens leaves to affect cold resistance. Analysis by nonparametric transcriptome sequencing revealed 2402 genes that were differentially expressed, most of which were related to resistance. Simultaneously, quantitative real-time polymerase chain reaction (qRT-PCR) analysis obtained results consistent with the transcriptome. These results indicated that O. patens could alter leaf structure and physiological and biochemical metabolic processes by initiating resistance-related molecular regulatory networks to improve the ability to resist cold stress. This study was the first to discuss the physiological, biochemical and molecular regulatory mechanisms of O. patens resistance to cold stress, which laid a foundation for revealing the biological and molecular mechanisms of overwintering of O. patens and breeding cold-resistant horticultural crops in northern China.
TM-Vec: template modeling vectors for fast homology detection and alignment
Authors: Hamamsy Tymor , Morton James T. , Berenberg Daniel , Carriero Nicholas , Gligorijevic Vladimir , Blackwell Robert , Strauss Charlie E. M. , Leman Julia Koehler , Cho Kyunghyun , Bonneau Richard 
Published: July 2022

AbstractExploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity ≤ 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.