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We also found that the two major methylation sites known to regulate TOP3B activity are located in the most conserved region of the C-terminal arginine-glycine-glycine (RGG) box, suggesting that a similar regulatory mechanism may operate throughout animals. Overall, our results provide a better understanding of the evolution and functional roles of TOP3B.DNA binding proteins recognize DNA specifically or non-specifically using direct and indirect readout mechanisms like sliding, hopping, and diffusion. However, a common difficulty in explicitly elucidating any particular mechanism of site-specific DNA-protein recognition is the lack of knowledge regarding target sequences and inadequate account of non-specific interactions, in general. Here, we decipher the structural basis of target search performed by the key regulator of expression of c-myc proto-oncogene, the human RBMS1 protein. In this study, we have shown the structural reorganization of this multi-domain protein required for recognizing the specific c-myc promoter sequence. The results suggest that a synergy between structural re-organization and thermodynamics is necessary for the recognition of target sequences. selleck compound The study presents another perspective of looking at the DNA-protein interactions.Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT's predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at http//protdomain.princeton.edu/dsprint.The DeepRefiner webserver, freely available at http//watson.cse.eng.auburn.edu/DeepRefiner/, is an interactive and fully configurable online system for high-accuracy protein structure refinement. Fuelled by deep learning, DeepRefiner offers the ability to leverage cutting-edge deep neural network architectures which can be calibrated for on-demand selection of adventurous or conservative refinement modes targeted at degree or consistency of refinement. The method has been extensively tested in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments under the group name 'Bhattacharya-Server' and was officially ranked as the No. 2 refinement server in CASP13 (second only to 'Seok-server' and outperforming all other refinement servers) and No. 2 refinement server in CASP14 (second only to 'FEIG-S' and outperforming all other refinement servers including 'Seok-server'). The DeepRefiner web interface offers a number of convenient features, including (i) fully customizable refinement job submission and validation; (ii) automated job status update, tracking, and notifications; (ii) interactive and interpretable web-based results retrieval with quantitative and visual analysis and (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips.Heterochromatin has essential functions in maintaining chromosome structure, in protecting genome integrity and in stabilizing gene expression programs. Heterochromatin is often nucleated by underlying DNA repeat sequences, such as major satellite repeats (MSR) and long interspersed nuclear elements (LINE). In order to establish heterochromatin, MSR and LINE elements need to be transcriptionally competent and generate non-coding repeat RNA that remain chromatin associated. We explored whether these heterochromatic RNA, similar to DNA and histones, may be methylated, particularly for 5-methylcytosine (5mC) or methyl-6-adenosine (m6A). Our analysis in mouse ES cells identifies only background level of 5mC but significant enrichment for m6A on heterochromatic RNA. Moreover, MSR transcripts are a novel target for m6A RNA modification, and their m6A RNA enrichment is decreased in ES cells that are mutant for Mettl3 or Mettl14, which encode components of a central RNA methyltransferase complex. Importantly, MSR transcripts that are partially deficient in m6A RNA methylation display impaired chromatin association and have a reduced potential to form RNADNA hybrids. We propose that m6A modification of MSR RNA will enhance the functions of MSR repeat transcripts to stabilize mouse heterochromatin.PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime.
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