I-Motif DNA: structural features and significance to cell biology.
Ontology highlight
ABSTRACT:
SUBMITTER: Abou Assi H
PROVIDER: S-EPMC6144788 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Publications
i-Motif DNA: structural features and significance to cell biology.
Abou Assi Hala H Garavís Miguel M González Carlos C Damha Masad J MJ
Nucleic acids research 20180901 16
The i-motif represents a paradigmatic example of the wide structural versatility of nucleic acids. In remarkable contrast to duplex DNA, i-motifs are four-stranded DNA structures held together by hemi- protonated and intercalated cytosine base pairs (C:C+). First observed 25 years ago, and considered by many as a mere structural oddity, interest in and discussion on the biological role of i-motifs have grown dramatically in recent years. In this review we focus on structural aspects of i-motif f ...[more]
Similar Datasets
Project description:Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
| S-EPMC8203844 | biostudies-literature
Project description:Three-dimensional macromolecular structures shed critical light on biological mechanism and facilitate development of small molecule inhibitors. Clinical success of raltegravir, a potent inhibitor of HIV-1 integrase, demonstrated the utility of this viral DNA recombinase as an antiviral target. A variety of partial integrase structures reported in the past 16 years have been instrumental and very informative to the field. Nonetheless, because integrase protein fragments are unable to functionally engage the viral DNA substrate critical for strand transfer inhibitor binding, the early structures did little to materially impact drug development efforts. However, recent results based on prototype foamy virus integrase have fully reversed this trend, as a number of X-ray crystal structures of active integrase-DNA complexes revealed key mechanistic details and moreover established the foundation of HIV-1 integrase strand transfer inhibitor action. In this review we discuss the landmarks in the progress of integrase structural biology during the past 17 years.
| S-EPMC3640404 | biostudies-literature
Project description:Histidine residues play crucial roles in shaping the function and structure of proteins due to their unique ability to act as both acids and bases. In other words, they can serve as proton donors and acceptors at physiological pH. This exceptional property is attributed to the side-chain imidazole ring of histidine residues. Consequently, determining the acid-base dissociation constant (Ka) of histidine imidazole rings in proteins often yields valuable insights into protein functions. Significant efforts have been dedicated to measuring the pKa values of histidine residues in various proteins, with nuclear magnetic resonance (NMR) spectroscopy being the most commonly used technique. However, NMR-based methods encounter challenges in assigning signals to individual imidazole rings and require a substantial amount of proteins. To address these issues associated with NMR-based approaches, a mass-spectrometry-based method known as histidine hydrogen-deuterium exchange mass spectrometry (His-HDX-MS) has been developed. This technique not only determines the pKa values of histidine imidazole groups but also quantifies their solvent accessibility. His-HDX-MS has proven effective across diverse proteins, showcasing its utility. This review aims to clarify the fundamental principles of His-HDX-MS, detail the experimental workflow, explain data analysis procedures and provide guidance for interpreting the obtained results.
| S-EPMC10813008 | biostudies-literature
Project description:MotivationPolyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA. Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.ResultsWe propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance. We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ~30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.Availabilityhttp://sfb.kaust.edu.sa/Pages/Software.aspx.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3694652 | biostudies-literature
Project description:Context:Variants of uncertain significance (VUSs) lack sufficient evidence, in terms of statistical power or experimental studies, to allow unequivocal determination of their damaging effect. VUSs are a major burden in performing genetic analysis. Although in silico prediction tools are widely used, their specificity is low, thus urgently calling for methods for prioritizing and characterizing variants. Objective:To assess the frequency of VUSs in genes causing endocrine and metabolic disorders, the concordance rate of predictions from different in silico methods, and the added value of three-dimensional protein structure analysis in discerning and prioritizing damaging variants. Results:A total of 12,266 missense variants reported in 641 genes causing endocrine and metabolic disorders were analyzed. Among these, 4123 (33.7%) were VUSs, of which 2010 (48.8%) were predicted to be damaging and 1452 (35.2%) were predicted to be tolerated according to in silico tools. A total of 5383 (87.7%) of 6133 disease-causing variants and 823 (55.8%) of 1474 benign variants were correctly predicted. In silico predictions were noninformative in 5.7%, 14.4%, and 16% of damaging, benign, and VUSs, respectively. A damaging effect on 3D protein structure was present in 240 (30.9%) of predicted damaging and 40 (9.7%) of predicted tolerated VUSs (P < 0.001). An in-depth analysis of nine VUSs occurring in TSHR, LDLR, CASR, and APOE showed that they greatly affect protein stability and are therefore strong candidates for disease. Conclusions:In our dataset, we confirmed the high sensitivity but low specificity of in silico predictions tools. 3D protein structural analysis is a compelling tool for characterizing and prioritizing VUSs and should be a part of genetic variant analysis.
| S-EPMC6041779 | biostudies-literature
Project description:Abasic (AP) sites are one of the most frequently occurring types of DNA damage. They lead to DNA strand breaks, interstrand DNA crosslinks, and block transcription and replication. Mutagenicity of AP sites arises from translesion synthesis (TLS) by error-prone bypass polymerases. Recently, a new cellular response to AP sites was discovered, in which the protein HMCES (5-hydroxymethlycytosine (5hmC) binding, embryonic stem cell-specific) forms a stable, covalent DNA-protein crosslink (DPC) to AP sites at stalled replication forks. The stability of the HMCES-DPC prevents strand cleavage by endonucleases and mutagenic bypass by TLS polymerases. Crosslinking is carried out by a unique SRAP (SOS Response Associated Peptidase) domain conserved across all domains of life. Here, we review the collection of recently reported SRAP crystal structures from human HMCES and E. coli YedK, which provide a unified basis for SRAP specificity and a putative chemical mechanism of AP site crosslinking. We discuss the structural and chemical basis for the stability of the SRAP DPC and how it differs from covalent protein-DNA intermediates in DNA lyase catalysis of strand scission.
| S-EPMC7494546 | biostudies-literature
Project description:The formation of adducts by the reaction of chemicals with DNA is a critical step for the initiation of carcinogenesis. The structural analysis of various DNA adducts reveals that conformational and chemical rearrangements and interconversions are a common theme. Conformational changes are modulated both by the nature of adduct and the base sequences neighboring the lesion sites. Equilibria between conformational states may modulate both DNA repair and error-prone replication past these adducts. Likewise, chemical rearrangements of initially formed DNA adducts are also modulated both by the nature of adducts and the base sequences neighboring the lesion sites. In this review, we focus on DNA damage caused by a number of environmental and endogenous agents, and biological consequences.
| S-EPMC3714022 | biostudies-literature
Project description:BackgroundThe triple-negative breast cancer (TNBC) constitutes a heterogeneous disease with an aggressive behavior and a poor prognosis. A better understanding of its biology is required to identify new biomarkers and improve clinical outcomes.SummaryTo date, the definition and classification of TNBC depends on a multiomic approach including immunohistochemistry (IHC), genomic, and transcriptomic features, and the tumor immune landscape. The development of new technologies has allowed us to sequence the whole cancer genome. The Cancer Genome Atlas (TCGA) and next-generation sequencing have led to a greater knowledge of DNA alterations such as TP53 or BRCA mutations, copy number variations, and DNA methylations. In addition, gene expression profiling has allowed to define a molecular intrinsic classification of TNBC based on mRNA. IHC and genomic profiling are also necessary to identify new immune biomarkers such as the presence of tumor-infiltrating lymphocytes and the expression of immune checkpoint molecules.Key messagesThis review aimed to provide recent knowledge of TNBC biology and classification focused on IHC, transcriptomics, genomic features, and the new immune biomarkers.
| S-EPMC7383258 | biostudies-literature
Project description:This work describes a method for predicting DNA binding function from structure using 3-dimensional templates. Proteins that bind DNA using small contiguous helix-turn-helix (HTH) motifs comprise a significant number of all DNA-binding proteins. A structural template library of seven HTH motifs has been created from non-homologous DNA-binding proteins in the Protein Data Bank. The templates were used to scan complete protein structures using an algorithm that calculated the root mean squared deviation (rmsd) for the optimal superposition of each template on each structure, based on C(alpha) backbone coordinates. Distributions of rmsd values for known HTH-containing proteins (true hits) and non-HTH proteins (false hits) were calculated. A threshold value of 1.6 A rmsd was selected that gave a true hit rate of 88.4% and a false positive rate of 0.7%. The false positive rate was further reduced to 0.5% by introducing an accessible surface area threshold value of 990 A2 per HTH motif. The template library and the validated thresholds were used to make predictions for target proteins from a structural genomics project.
| S-EPMC156721 | biostudies-literature
Project description:Autophagy is a homeostatic process common to all eukaryotic cells that serves to degrade intracellular components. Among three classes of autophagy, macroautophagy is best understood, and is the subject of this Review. The function of autophagy is multifaceted, and includes removal of long-lived proteins and damaged or unneeded organelles, recycling of intracellular components for nutrients, and defense against pathogens. This process has been extensively studied in yeast, and understanding of its functional significance in human disease is also increasing. This Review explores the basic machinery and regulation of autophagy in mammalian systems, methods employed to measure autophagic activity, and then focuses on recent discoveries about the functional significance of autophagy in respiratory diseases, including chronic obstructive pulmonary disease, cystic fibrosis, tuberculosis, idiopathic pulmonary fibrosis, pulmonary arterial hypertension, acute lung injury, and lymphangioleiomyomatosis.
| S-EPMC3547078 | biostudies-literature