Modeling Coronavirus Spike Protein Dynamics: Implications for Immunogenicity and Immune Escape

Kunkel, Genevieve; Madani, Mohammad; White, Simon; Verardi, Paulo H.; Tarakanova, Anna

doi:10.1101/2021.08.19.456973

Cited by 6 publications

(9 citation statements)

References 82 publications

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“…This subnet includes a series of SE-Concat blocks (SI Figure S3B), that pass the extracted feature map to the synthesis subnet gradually to generate the protein contact map 40,41 . SE-Concat is inspired from the SE-ResNet architecture, modified to use concatenation instead of summation, to reuse the extracted features and increase the information flow between layers 40,42,43 . The squeeze excitation (SE) block (SI Figure S3D) within the SE-Concat block emphasizes the important information and improves channel interdependencies 41 .…”

Section: Methodsmentioning

confidence: 99%

CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks

Madani

Behzadi

Song

et al. 2022

Preprint

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Protein contact maps represent spatial pairwise inter-residue interactions, providing a protein's translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure prediction, one of computational biology's most challenging problems in the last half-century. While many computational tools have been developed to this end, most fail to predict accurate contact maps for proteins with insufficient homologous protein sequences, and exhibit low accuracy for long-range contacts. To address these limitations, we develop a novel hybrid model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we build the generator of CGAN-Cmap via two parallel modules: sequential and pairwise modules to capture and interpret distance profiles from 1D sequential and 2D pairwise feature maps, respectively, and combine them during the training process to generate the contact map. This novel architecture helps to improve the contact map prediction by surpassing redundant features and encouraging more meaningful ones from 1D and 2D inputs simultaneously. We also introduce a new custom dynamic binary cross-entropy (BCE) as the loss function to extract essential details from feature maps, and thereby address the input imbalance problem for highly sparse long-range contacts in proteins with insufficient numbers of homologous sequences. We evaluate the performance of CGAN-Cmap on the 11th, 12th, 13th, and 14th Critical Assessment of protein Structure Prediction (CASP 11, 12, 13, and 14) and CAMEO test sets. CGAN-Cmap significantly outperforms state-of-the-art models, and in particular, it improves the precision of medium and long-range contact by at least 3.5%. Furthermore, our model has a low dependency on the number of homologous sequences obtained via multiple sequence alignment, suggesting that it can predict protein contact maps with good accuracy for those proteins that lack homologous templates. These results demonstrate an efficient approach for fast and highly accurate contact map prediction toward construction of protein 3D structure from protein sequence.

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Section: Methodsmentioning

confidence: 99%

CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks

Madani

Behzadi

Song

et al. 2022

Preprint

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“…After the features are extracted, to check whether there are pedestrians in those candidate areas or not, neural networks have been used. This type of neural network is made by imposing restrictions on weights and how to connect the neurons of a standard neural network and creates a network suitable for processing data with temporal or spatial distribution [30]. In another study, pedestrians were recognized based on gradient and texture features.…”

Section: Related Workmentioning

confidence: 99%

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Ya-meng¹,

Wang²,

Shen-shen³

2022

IJACSA

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The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).

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“…Kunkel et al [19] investigated the Modeling coronavirus spike protein flow: suggestions for immunogenicity and safe elude. Hosseini et al [20] reviewed the creation of an Al/Mg bimetallic thin-walled ultrafine-grained tube by extreme plastic distortion diary of materials building and execution.…”

Section: Introductionmentioning

confidence: 99%

Chemical Reaction-Diffusion Model Around a Vessel for Studying Temperature and Concentration of Three Chemical Species by Finite Element Method

Pasha

Taghinia

Chari

et al. 2023

IJE

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In this paper, the temperature and concentration of species around a vessel using the reaction and diffusion relations were investigated. The reactions between 3 chemical species, and the relationship between temperature changes and the rate of chemical reactions were studied. The novelty of this paper is the use of different coefficients of material with diffusion constants and also considering the concentration and temperature of materials involved in the reaction with non-heat sources and with heat source modes. So that showed the concentration and heat transfer rate of substances involved in the chemical reactions in the form of two-dimensional and three-dimensional diagrams about their distance from the borders of the vessel. The finite element method is utilized for calculated differential equations. According to the results obtained, when the temperature of the reactants increased more heat is released; the concentration also changed a lot, and its amount increased. However, in products such as substance (c), it has an inverse relationship with reactants (a) and (b) in such a way that as the concentration and temperature of the reactants increased, these values decreased in the product. On average, concentration changes in the distance from the center to the surroundings the maximum heat source mode was about 76% less than the average heat source mode and about 14% less than the non-heat source mode.

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Modeling Coronavirus Spike Protein Dynamics: Implications for Immunogenicity and Immune Escape

Cited by 6 publications

References 82 publications

CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks

CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Chemical Reaction-Diffusion Model Around a Vessel for Studying Temperature and Concentration of Three Chemical Species by Finite Element Method

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