scholarly journals Relationships between IgE/IgG4 Epitopes, Structure and Function in Anisakis simplex Ani s 5, a Member of the SXP/RAL-2 Protein Family

2014 ◽  
Vol 8 (3) ◽  
pp. e2735 ◽  
Author(s):  
María Flor García-Mayoral ◽  
Miguel Angel Treviño ◽  
Teresa Pérez-Piñar ◽  
María Luisa Caballero ◽  
Tobias Knaute ◽  
...  
2020 ◽  
Author(s):  
Dylan Marshall ◽  
Haobo Wang ◽  
Michael Stiffler ◽  
Justas Dauparas ◽  
Peter Koo ◽  
...  

AbstractIf disentangled properly, patterns distilled from evolutionarily related sequences of a given protein family can inform their traits - such as their structure and function. Recent years have seen an increase in the complexity of generative models towards capturing these patterns; from sitewise to pairwise to deep and variational. In this study we evaluate the degree of structure and fitness patterns learned by a suite of progressively complex models. We introduce pairwise saliency, a novel method for evaluating the degree of captured structural information. We also quantify the fitness information learned by these models by using them to predict the fitness of mutant sequences and then correlate these predictions against their measured fitness values. We observe that models that inform structure do not necessarily inform fitness and vice versa, contrasting recent claims in this field. Our work highlights a dearth of consistency across fitness assays as well as divergently provides a general approach for understanding the pairwise decomposable relations learned by a given generative sequence model.


2020 ◽  
Author(s):  
Nan Zhang ◽  
Chunyan Zhao ◽  
Xinxin Zhang ◽  
Xiaoteng Cui ◽  
Yan Zhao ◽  
...  

2016 ◽  
Vol 1864 (5) ◽  
pp. 570-583 ◽  
Author(s):  
Manish Goyal ◽  
Chinmoy Banerjee ◽  
Shiladitya Nag ◽  
Uday Bandyopadhyay

2021 ◽  
Author(s):  
Pengshuo Yang ◽  
Wei Zheng ◽  
Kang Ning ◽  
Yang Zhang

Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil and Fermentor), we proposed a MetaSource model to decode the inherent link of microbial niches with protein homologous families. Large-scale protein family folding experiments showed that a targeted approach using predicted biomes significantly outperform combined metagenome datasets in both speed of MSA collection and accuracy of deep-learning structure assembly. These results revealed the important link of biomes with protein families and provided a useful bluebook to guide future microbiome sequence database and modeling development for protein structure and function prediction.


2007 ◽  
Vol 120 (14) ◽  
pp. 2366-2377 ◽  
Author(s):  
G. Gaidos ◽  
S. Soni ◽  
D. J. Oswald ◽  
P. A. Toselli ◽  
K. H. Kirsch

2013 ◽  
Vol 35 (10) ◽  
pp. 1189-1197 ◽  
Author(s):  
Bao-Zhu LI ◽  
Xiang ZHAO ◽  
Xiao-Liang ZHAO ◽  
Lei PENG

2017 ◽  
Vol 12 (9) ◽  
pp. 1551 ◽  
Author(s):  
De-en Xu ◽  
Yan Zou ◽  
Wei-feng Zhang ◽  
Hai-ying Liu ◽  
Xia Li ◽  
...  

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