scholarly journals A novel algorithm of the digital nervous tissue phantom creation based on 3D Voronoi diagram application

2021 ◽  
Vol 2090 (1) ◽  
pp. 012009
Author(s):  
Y.R. Nartsissov
Keyword(s):  
Nervous Tissue ◽  
De Novo ◽  
3D Structure ◽  
Reaction Diffusion ◽  
Problem Formulation ◽  
Numerical Calculations ◽  
Virtual Object ◽  
Tissue Phantom ◽  
Digital Phantom ◽  
Smooth Objects

Abstract The essential part of mathematical modelling of nutrients convectional reaction-diffusion is creation of a digital phantom of considered biological object. This process becomes an especial problem which needs to be solved before numerical calculations of the concentration gradients will be done. There are two principal ways to get the solution in this case. The first approach is the reconstruction of a digital phantom on the base of the experimental data directly. The second one is the creation of a virtual object according to the experimental evidence and the known principals de novo. The main advantage of the created phantom is a high adaptability to modelling demands and a physical problem formulation. In the present study a new algorithm of a digital phantom creation has been established. The principles of the claimed procedures are demonstrated by the example of a nervous tissue. Initially, one needs to create N 3D objects according to Voronoi diagrams. Each object has 144 edges and 69 boundaries on average. Having chosen M rear objects, a long 3D structure mimicking neurons axons is created according to a loft procedure from the start boundaries to the end ones. Then, the set of Boolean operations has been applied to form continuous smooth objects. The remain (N-(M+s)) objects are combined into several whole bodies using the loft procedures between the closet neighbours. The final structure has a good conformity with a nervous tissue architecture. Furthermore, the obtained phantom is correct to the mesh application and further numerical calculations.

scholarly journals Novel Dominant KCNQ2 Exon 7 Partial In-Frame Duplication in a Complex Epileptic and Neurodevelopmental Delay Syndrome

2020 ◽  
Vol 21 (12) ◽  
pp. 4447
Author(s):  
Pedro A. Lazo ◽  
Juan L. García ◽  
Paulino Gómez-Puertas ◽  
Íñigo Marcos-Alcalde ◽  
Cesar Arjona ◽  
...  
Keyword(s):  
Protein Function ◽  
De Novo ◽  
3D Structure ◽  
Unknown Origin ◽  
Unknown Etiology ◽  
Neurodevelopmental Delay ◽  
Calmodulin Binding ◽  
Whole Exome ◽  
Dynamics Simulations ◽  
Frame Duplication

Complex neurodevelopmental syndromes frequently have an unknown etiology, in which genetic factors play a pathogenic role. This study utilizes whole-exome sequencing (WES) to examine four members of a family with a son presenting, since birth, with epileptic-like crises, combined with cerebral palsy, severe neuromotor and developmental delay, dystonic tetraparexia, axonal motor affectation, and hyper-excitability of unknown origin. The WES study detected within the patient a de novo heterozygous in-frame duplication of thirty-six nucleotides within exon 7 of the human KCNQ2 gene. This insertion duplicates the first twelve amino acids of the calmodulin binding site I. Molecular dynamics simulations of this KCNQ2 peptide duplication, modelled on the 3D structure of the KCNQ2 protein, suggest that the duplication may lead to the dysregulation of calcium inhibition of this protein function.

scholarly journals Structure of SPH (self-incompatibility protein homologue) proteins: a widespread family of small, highly stable, secreted proteins

2019 ◽  
Vol 476 (5) ◽  
pp. 809-826
Author(s):  
Karthik V. Rajasekar ◽  
Shuangxi Ji ◽  
Rachel J. Coulthard ◽  
Jon P. Ride ◽  
Gillian L. Reynolds ◽  
...  
Keyword(s):  
Fusion Protein ◽  
Analytical Ultracentrifugation ◽  
De Novo ◽  
Learning Algorithm ◽  
Sequence Similarity ◽  
Homology Modelling ◽  
3D Structure ◽  
Large Family ◽  
Secreted Proteins ◽  
Self Incompatibility

Abstract SPH (self-incompatibility protein homologue) proteins are a large family of small, disulfide-bonded, secreted proteins, initially found in the self-incompatibility response in the field poppy (Papaver rhoeas), but now known to be widely distributed in plants, many containing multiple members of this protein family. Using the Origami strain of Escherichia coli, we expressed one member of this family, SPH15 from Arabidopsis thaliana, as a folded thioredoxin fusion protein and purified it from the cytosol. The fusion protein was cleaved and characterised by analytical ultracentrifugation, circular dichroism and nuclear magnetic resonance (NMR) spectroscopy. This showed that SPH15 is monomeric and temperature stable, with a β-sandwich structure. The four strands in each sheet have the same topology as the unrelated proteins: human transthyretin, bacterial TssJ and pneumolysin, with no discernible sequence similarity. The NMR-derived structure was compared with a de novo model, made using a new deep learning algorithm based on co-evolution/correlated mutations, DeepCDPred, validating the method. The DeepCDPred de novo method and homology modelling to SPH15 were then both used to derive models of the 3D structure of the three known PrsS proteins from P. rhoeas, which have only 15–18% sequence homology to SPH15. The DeepCDPred method gave models with lower discreet optimised protein energy scores than the homology models. Three loops at one end of the poppy structures are postulated to interact with their respective pollen receptors to instigate programmed cell death in pollen tubes.

scholarly journals Recent Applications of Retro-Inverso Peptides

2021 ◽  
Vol 22 (16) ◽  
pp. 8677
Author(s):  
Nunzianna Doti ◽  
Mario Mardirossian ◽  
Annamaria Sandomenico ◽  
Menotti Ruvo ◽  
Andrea Caporale
Keyword(s):  
Amino Acids ◽  
Neurodegenerative Diseases ◽  
De Novo ◽  
3D Structure ◽  
New Drugs ◽  
Side Chains ◽  
Pros And Cons ◽  
Endogenous Proteases ◽  
Natural Amino Acids ◽  
Poor Stability

Natural and de novo designed peptides are gaining an ever-growing interest as drugs against several diseases. Their use is however limited by the intrinsic low bioavailability and poor stability. To overcome these issues retro-inverso analogues have been investigated for decades as more stable surrogates of peptides composed of natural amino acids. Retro-inverso peptides possess reversed sequences and chirality compared to the parent molecules maintaining at the same time an identical array of side chains and in some cases similar structure. The inverted chirality renders them less prone to degradation by endogenous proteases conferring enhanced half-lives and an increased potential as new drugs. However, given their general incapability to adopt the 3D structure of the parent peptides their application should be careful evaluated and investigated case by case. Here, we review the application of retro-inverso peptides in anticancer therapies, in immunology, in neurodegenerative diseases, and as antimicrobials, analyzing pros and cons of this interesting subclass of molecules.

Receptor-Based Models with Diffusion-Driven Instability for Pattern Formation in Hydra

2003 ◽  
Vol 11 (03) ◽  
pp. 293-324 ◽  
Author(s):  
Anna Marciniak-Czochra
Keyword(s):  
Pattern Formation ◽  
Feedback Loop ◽  
De Novo ◽  
Initial Conditions ◽  
Reaction Diffusion ◽  
Reaction Diffusion Equations ◽  
Variable Model ◽  
Dissociated Cells ◽  
Cutting Experiments ◽  
Positional Value

The aim of this paper is to show under which conditions a receptor-based model can produce and regulate patterns. Such model is applied to the pattern formation and regulation in a fresh water polyp, hydra. The model is based on the idea that both head and foot formation could be controlled by receptor-ligand binding. Positional value is determined by the density of bound receptors. The model is defined in the form of reaction-diffusion equations coupled with ordinary differential equations. The objective is to check what minimal processes are sufficient to produce patterns in the framework of a diffusion-driven (Turing-type) instability. Three-variable (describing the dynamics of ligands, free and bound receptors) and four-variable models (including also an enzyme cleaving the ligand) are analyzed and compared. The minimal three-variable model takes into consideration the density of free receptors, bound receptors and ligands. In such model patterns can evolve only if self-enhancement of free receptors, i.e., a positive feedback loop between the production of new free receptors and their present density, is assumed. The final pattern strongly depends on initial conditions. In the four-variable model a diffusion-driven instability occurs without the assumption that free receptors stimulate their own synthesis. It is shown that gradient in the density of bound receptors occurs if there is also a second diffusible substance, an enzyme, which degrades ligands. Numerical simulations are done to illustrate the analysis. The four-variable model is able to capture some results from cutting experiments and reflects de novo pattern formation from dissociated cells.

Modeling the 3D Structure of Rhodopsin Using a De Novo Approach to Build G-protein−Coupled Receptors

1999 ◽  
Vol 103 (13) ◽  
pp. 2520-2527 ◽  
Author(s):  
Marta Filizola ◽  
Maria Cartenì-Farina ◽  
Juan J. Perez
Keyword(s):  
G Protein ◽  
De Novo ◽  
3D Structure ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled

scholarly journals PredMP: a web server for de novo prediction and visualization of membrane proteins

2018 ◽  
Vol 35 (4) ◽  
pp. 691-693 ◽  
Author(s):  
Sheng Wang ◽  
Shiyang Fei ◽  
Zongan Wang ◽  
Yu Li ◽  
Jinbo Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lipid Bilayer ◽  
3D Model ◽  
De Novo ◽  
3D Structure ◽  
Supplementary Information ◽  
Model Generation ◽  
Transmembrane Topology ◽  
Distance Restraints ◽  
Deep Learning Model

Abstract Motivation PredMP is the first web service, to our knowledge, that aims at de novo prediction of the membrane protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualization. Our approach is based on a high-throughput Deep Transfer Learning (DTL) method that first predicts MP contacts by learning from non-MPs and then predicts the 3D model of the MP using the predicted contacts as distance restraints. This algorithm is derived from our previous Deep Learning (DL) method originally developed for soluble protein contact prediction, which has been officially ranked No. 1 in CASP12. The DTL framework in our approach overcomes the challenge that there are only a limited number of solved MP structures for training the deep learning model. There are three modules in the PredMP server: (i) The DTL framework followed by the contact-assisted folding protocol has already been implemented in RaptorX-Contact, which serves as the key module for 3D model generation; (ii) The 1D annotation module, implemented in RaptorX-Property, is used to predict the secondary structure and disordered regions; and (iii) the visualization module to display the predicted MPs embedded in the lipid bilayer guided by the predicted transmembrane topology. Results Tested on 510 non-redundant MPs, our server predicts correct folds for ∼290 MPs, which significantly outperforms existing methods. Tested on a blind and live benchmark CAMEO from September 2016 to January 2018, PredMP can successfully model all 10 MPs belonging to the hard category. Availability and implementation PredMP is freely accessed on the web at http://www.predmp.com. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences

2015 ◽  
Vol 112 (17) ◽  
pp. 5413-5418 ◽  
Author(s):  
Sikander Hayat ◽  
Chris Sander ◽  
Debora S. Marks ◽  
Arne Elofsson
Keyword(s):  
Structure Prediction ◽  
De Novo ◽  
3D Structure ◽  
3D Models ◽  
Sequence Information ◽  
Sequence Alignments ◽  
Residue Contacts ◽  
Machine Learning Approach ◽  
3D Structure Prediction ◽  
Structure Accuracy

Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand–strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

scholarly journals 2018 YPIC Challenge: A case study in characterizing an unknown protein sample

2019 ◽  
Author(s):  
Lindsay Pino ◽  
Andy Lin ◽  
Wout Bittremieux
Keyword(s):  
Protein Sequence ◽  
De Novo ◽  
Signal To Noise Ratio ◽  
3D Structure ◽  
Sequence Information ◽  
Sequence Coverage ◽  
Post Translational Modifications ◽  
E Coli ◽  
Unknown Protein ◽  
Synthetic Protein

For the 2018 YPIC Challenge contestants were invited to try to decipher two unknown English questions encoded by a synthetic protein expressed in Escherichia coli. In addition to deciphering the sentence, contestants were asked to determine the 3D structure and detect any post-translation modifications left by the host organism. We present our experimental and computational strategy to characterize this sample by identifying the unknown protein sequence and detecting the presence of post-translational modifications. The sample was acquired with dynamic exclusion disabled to increase the signal-to-noise ratio of the measured molecules, after which spectral clustering was used to generate high-quality consensus spectra. De novo spectrum identification was used to determine the synthetic protein sequence, and any post-translational modifications introduced by E. coli on the synthetic protein were analyzed via spectral networking. This workflow resulted in a de novo sequence coverage of 70%, on par with sequence database searching performance. Additionally, the spectral networking analysis indicated that no systematic modifications were introduced on the synthetic protein by E. coli. The strategy presented here can be directly used to analyze samples for which no protein sequence information is available or when the identity of the sample is unknown. All software and code to perform the bioinformatics analysis is available as open source, and self-contained Jupyter notebooks are provided to fully recreate the analysis.

De Novo 3D Structure Determination from Sub-milligram Protein Samples by Solid-State 100 kHz MAS NMR Spectroscopy

2014 ◽  
Vol 53 (45) ◽  
pp. 12253-12256 ◽  
Author(s):  
Vipin Agarwal ◽  
Susanne Penzel ◽  
Kathrin Szekely ◽  
Riccardo Cadalbert ◽  
Emilie Testori ◽  
...  
Keyword(s):  
Solid State ◽  
Nmr Spectroscopy ◽  
Structure Determination ◽  
De Novo ◽  
3D Structure ◽  
Mas Nmr ◽  
3D Structure Determination

Growth of untreated vestibular schwannoma: a prospective study

2012 ◽  
Vol 116 (4) ◽  
pp. 706-712 ◽  
Author(s):  
Jobin Kotakkathu Varughese ◽  
Cathrine Nansdal Breivik ◽  
Tore Wentzel-Larsen ◽  
Morten Lund-Johansen
Keyword(s):  
Prospective Study ◽  
Doubling Time ◽  
De Novo ◽  
Growth Models ◽  
Care Center ◽  
Tertiary Care ◽  
3D Structure ◽  
Growth Dynamics ◽  
Volume Doubling Time ◽  
Volume Doubling

Object Small vestibular schwannomas (VSs) are often conservatively managed and treated only upon growth. Growth is usually reported in mm/year, but describing the growth of a 3D structure by a single diameter has been questioned. As a result, VS growth dynamics should be further investigated. In addition, baseline clinical parameters that could predict growth would be helpful. In this prospective study the authors aimed to describe growth dynamics in a cohort of conservatively managed VSs. They also compared different growth models and evaluated the ability of baseline parameters to predict future growth. Methods Between 2000 and 2006, 178 consecutive patients with unilateral de novo small-sized VSs identified among the Norwegian population of 4.8 million persons were referred to a tertiary care center and were included in a study protocol of conservative management. Tumor size was defined by MR imaging–based volume estimates and was recorded along with clinical data at regular visits. Mixed-effects models were used to analyze the relationships between observations. Three growth models were compared using statistical diagnostic tests: a mm/year–based model, a cm3/year–based model, and a volume doubling time (VDT)-based model. A receiver operating characteristic curve analysis was used to determine a cutoff for the VDT-based model for distinguishing growing and nongrowing tumors. Results A mean growth rate corresponding to a VDT of 4.40 years (95% CI 3.49–5.95) was found. Other growth models in this study revealed mean growth rates of 0.66 mm/year (95% CI 0.47–0.86) and 0.19 cm3/year (95% CI 0.12–0.26). Volume doubling time was found to be the most realistic growth model. All baseline variables had p values > 0.09 for predicting growth. Conclusions Based on the actual measurements, VDT was the most correct way to describe VS growth. The authors found that a cutoff of 5.22 years provided the best value to distinguish growing from nongrowing tumors. None of the investigated baseline predictors were usable as predictors of growth.

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