scholarly journals Structure-Based Understanding of ABCA3 Variants

2021 ◽  
Vol 22 (19) ◽  
pp. 10282
Author(s):  
Marion Onnée ◽  
Pascale Fanen ◽  
Isabelle Callebaut ◽  
Alix de Becdelièvre
Keyword(s):  
Pulmonary Surfactant ◽  
Theoretical Study ◽  
3D Structure ◽  
Medical Decision ◽  
Structure Information ◽  
Regulatory Domains ◽  
Binding Domains ◽  
Disease Mutations ◽  
Neonatal Respiratory Distress ◽  
Experimental Structure

ABCA3 is a crucial protein of pulmonary surfactant biosynthesis, associated with recessive pulmonary disorders such as neonatal respiratory distress and interstitial lung disease. Mutations are mostly private, and accurate interpretation of variants is mandatory for genetic counseling and patient care. We used 3D structure information to complete the set of available bioinformatics tools dedicated to medical decision. Using the experimental structure of human ABCA4, we modeled at atomic resolution the human ABCA3 3D structure including transmembrane domains (TMDs), nucleotide-binding domains (NBDs), and regulatory domains (RDs) in an ATP-bound conformation. We focused and mapped known pathogenic missense variants on this model. We pinpointed amino-acids within the NBDs, the RDs and within the interfaces between the NBDs and TMDs intracellular helices (IHs), which are predicted to play key roles in the structure and/or the function of the ABCA3 transporter. This theoretical study also highlighted the possible impact of ABCA3 variants in the cytosolic part of the protein, such as the well-known p.Glu292Val and p.Arg288Lys variants.

scholarly journals TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

2021 ◽  
Vol 13 (12) ◽  
pp. 2255
Author(s):  
Matteo Pardini ◽  
Victor Cazcarra-Bes ◽  
Konstantinos Papathanassiou
Keyword(s):  
Information Content ◽  
Forest Structure ◽  
Structural Information ◽  
3D Structure ◽  
Physical Structure ◽  
Structure Mapping ◽  
Structure Information ◽  
Forest Sites ◽  
L Band ◽  
Structure Indices

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany.

scholarly journals Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

2020 ◽  
Author(s):  
Amelia Villegas-Morcillo ◽  
Stavros Makrodimitris ◽  
Roeland C H J van Ham ◽  
Angel M Gomez ◽  
Victoria Sanchez ◽  
...  
Keyword(s):  
Protein Function ◽  
Prediction Models ◽  
Protein Function Prediction ◽  
3D Structure ◽  
Function Prediction ◽  
Feature Representation ◽  
Training Data ◽  
Supplementary Information ◽  
Molecular Function ◽  
Structure Information

Abstract Motivation Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. Results We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining. Availability and implementation Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Expression of Pulmonary Surfactant-Associated Protein B in Neonatal Respiratory Distress Syndrome

2013 ◽  
Vol 32 (2) ◽  
pp. 146-151
Author(s):  
Xiaojuan Yin ◽  
Yan Wang ◽  
Lu Xie ◽  
Xiangyong Kong ◽  
Chunzhi Wang ◽  
...  
Keyword(s):  
Protein Expression ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Mrna Expression ◽  
Pulmonary Surfactant ◽  
Distress Syndrome ◽  
Neonatal Respiratory Distress Syndrome ◽  
Control Group ◽  
Neonatal Respiratory Distress ◽  
Protein B

Summary Background: The aim of this study was to investigate the role of pulmonary surfactant-associated protein B (SP-B) expression in the pathogenesis of neonatal respiratory distress syndrome (RDS) via detecting the protein and mRNA expression of SP-B. Methods: A total of 60 unrelated neonates who died of RDS were chosen as the RDS group and then subgrouped into ≤32 weeks group, 32∼37 weeks group and ≥37 weeks group (n=20). Sixty neonates who died of other diseases were enrolled as controls and subdivided into 3 matched groups based on the gestational age. Western blot assay and RT-PCR were employed. Results: In the RDS group, SP-B protein expression was reduced or deficient in 8 neonates of which 6 had no SP-B protein expression. In the control group, only 1 had reduced SP-B protein expression. The reduced or deficient SP-B protein expression in 9 neonates of both groups was noted in the ≥37 weeks group. In the RDS group, the SP-B mRNA expression was significantly lower than that in the control group. In the ≤37 weeks group, SP-B mRNA expression was comparable between the RDS group and control group. In the 32∼37 weeks group, the SP-B mRNA expression in the RDS group was significantly reduced when compared with the control group. In the ≥37 weeks group, the SP-B mRNA expression in the RDS group was dramatically lower than that in the control group. Conclusions: Alteration of SP-B expression is present at transcriptional and translational levels. Reduction of SP-B mRNA and protein expression is involved in the pathogenesis of RDS.

scholarly journals Recurrent evolution of vertebrate transcription factors by transposase capture

Science ◽  
2021 ◽  
Vol 371 (6531) ◽  
pp. eabc6405 ◽  
Author(s):  
Rachel L. Cosby ◽  
Julius Judd ◽  
Ruiling Zhang ◽  
Alan Zhong ◽  
Nathaniel Garry ◽  
...  
Keyword(s):  
Fusion Proteins ◽  
Regulatory Elements ◽  
Exon Shuffling ◽  
Cellular Functions ◽  
Protein Coding ◽  
Regulatory Domains ◽  
Dna Binding Domains ◽  
Binding Domains ◽  
Genome Wide ◽  
Tetrapod Evolution

Genes with novel cellular functions may evolve through exon shuffling, which can assemble novel protein architectures. Here, we show that DNA transposons provide a recurrent supply of materials to assemble protein-coding genes through exon shuffling. We find that transposase domains have been captured—primarily via alternative splicing—to form fusion proteins at least 94 times independently over the course of ~350 million years of tetrapod evolution. We find an excess of transposase DNA binding domains fused to host regulatory domains, especially the Krüppel-associated box (KRAB) domain, and identify four independently evolved KRAB-transposase fusion proteins repressing gene expression in a sequence-specific fashion. The bat-specific KRABINER fusion protein binds its cognate transposons genome-wide and controls a network of genes and cis-regulatory elements. These results illustrate how a transcription factor and its binding sites can emerge.

scholarly journals DENIES: A deep learning based two-layer predictor for enhancing the identification of enhancers and their strength with DNA shape information

2021 ◽  
Author(s):  
Li Ye ◽  
Chunquan Li ◽  
Jiquan Ma
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
3D Structure ◽  
Regulatory Elements ◽  
Ensemble Classifier ◽  
Performance Comparison ◽  
Shape Information ◽  
Structure Information ◽  
Dna Shape ◽  
Enhancer Identification

The identification of enhancers has always been an important task in bioinformatics owing to their major role in regulating gene expression. For this reason, many computational algorithms devoted to enhancer identification have been put forward over the years, ranging from statistics and machine learning to the increasing popular deep learning. To boost the performance of their methods, more features tend to be extracted from the single DNA sequences and integrated to develop an ensemble classifier. Nevertheless, the sequence-derived features used in previous studies can hardly provide the 3D structure information of DNA sequences, which is regarded as an important factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Given that, we here propose DENIES, a deep learning based two-layer predictor for enhancing the identification of enhancers and their strength. Besides two common sequence-derived features (i.e. one-hot and k-mer), it introduces DNA shape for describing the 3D structures of DNA sequences. The results of performance comparison with a series of state-of-the-art methods conducted on the same datasets prove the effectiveness and robustness of our method. The code implementation of our predictor is freely available at https://github.com/hlju-liye/DENIES.

scholarly journals Detailed Analysis of 17β-Estradiol-Aptamer InteractionsAptamer Interactions: A Molecular Dynamics Simulation Study

2018 ◽  
Author(s):  
Alexander Eisold ◽  
Dirk Labudde
Keyword(s):  
Md Simulation ◽  
3D Structure ◽  
Dynamics Simulation ◽  
Coarse Grained ◽  
Negative Effects ◽  
Time Step ◽  
Binding Characteristics ◽  
Structure Information ◽  
Potential Binding ◽  
Interior Loop

Micro-pollutants such as 17β-Estradiol (E2) have been detected in different water resources and their negative effects on the environment and organisms have been observed. Aptamers are established as a possible detection tool, but the underlying ligand binding is largely unexplored. In this study, a previously described 35-mer E2-specific aptamer was used to analyse the binding characteristics between E2 and the aptamer with a MD simulation in an aqueous medium. Because there is no 3D structure information available for this aptamer, it was modeled using coarse-grained modeling method. The E2 ligand was positioned inside a potential binding area of the predicted aptamer structure, the complex was used for an 25 ns MD simulation, and the interactions were examined for each time step. We identified E2-specific bases within the interior loop of the aptamer and also demonstrated the influence of frequently underestimated water-mediated hydrogen bonds. The study contributes to the understanding of the behavior of ligands binding with aptamer structure in an aqueous solution. The developed workflow allows generating and examining further appealing ligand-aptamer complexes.

scholarly journals Practical Considerations in the Selection and Use of Pulmonary Surfactant Therapy for Neonatal Respiratory Distress Syndrome in the Intensive Care Setting

2006 ◽  
Vol 11 (3) ◽  
pp. 161-168
Author(s):  
Karen E. Corff ◽  
Steve Greubel ◽  
Debra L. McCann ◽  
Richard Williams ◽  
Dwight L. Varner
Keyword(s):  
Adverse Effects ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Nurse Practitioners ◽  
Pulmonary Surfactant ◽  
Distress Syndrome ◽  
Response To Therapy ◽  
Lung Damage ◽  
Neonatal Respiratory Distress Syndrome ◽  
Neonatal Respiratory Distress

Pulmonary surfactant is the treatment of choice for neonatal respiratory distress syndrome, as it significantly reduces infant morbidity and mortality. Extensive clinical trials compare the surfactant products and their optimal usage, but often the practical administration issues are less frequently discussed. Herein, a panel of respiratory therapists and neonatal nurse practitioners share their experience regarding surfactant usage. According to the panelists, the primary criteria for surfactant selection are the ability to rapidly decrease ventilatory requirements toward extubation, a low incidence of adverse effects, cost-effectiveness, and ease of use. In most cases, surfactant is most efficacious when given as early as possible where indicated. The surfactant products differ in their storage, handling, preparation, and administration traits, and this may affect rapid dosing of the surfactant during acute treatment. During and after administration, optimal response to therapy depends on efficient management of ventilator settings, which requires vigilant monitoring of the infant. Common adverse effects include endotracheal tube reflux, bradycardia, and desaturation. Using a surfactant which requires a small dosing volume may decrease the incidence of these adverse effects. An emerging trend in clinical practice is the quick extubation of the infant to nasal continuous positive airway pressure after surfactant administration. This practice can reduce the need for ventilation and reduce the risk of ventilator-related lung damage. Nebulization of surfactant may be a future avenue of delivery, but further research is required to determine its precise role. The practical considerations summarized in this discussion may be useful for other clinicians in their own practice.

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