scholarly journals 1P002 Domain structure and function of novel transcriptional regulatory protein FMBP-1

2004 ◽  
Vol 44 (supplement) ◽  
pp. S30
Author(s):  
D. Matsumoto ◽  
T. Yamaki ◽  
K. Kawaguchi ◽  
H. Yamamoto ◽  
Y. Kumaki ◽  
...  
Keyword(s):  
Domain Structure ◽  
Regulatory Protein ◽  
Structure And Function ◽  
Transcriptional Regulatory ◽  
And Function

scholarly journals Structure and function of an ectopic Polycomb chromatin domain

2019 ◽  
Vol 5 (1) ◽  
pp. eaau9739 ◽  
Author(s):  
Sandip De ◽  
Yuzhong Cheng ◽  
Ming-an Sun ◽  
Natalie D. Gehred ◽  
Judith A. Kassis
Keyword(s):  
Domain Structure ◽  
Regulatory Elements ◽  
Structure And Function ◽  
Polycomb Group ◽  
Chromatin Domain ◽  
Repressive Mark ◽  
Group Response ◽  
Dna Elements ◽  
And Function

Polycomb group proteins (PcGs) drive target gene repression and form large chromatin domains. InDrosophila, DNA elements known as Polycomb group response elements (PREs) recruit PcGs to the DNA. We have shown that, within theinvected-engrailed(inv-en) Polycomb domain, strong, constitutive PREs are dispensable for Polycomb domain structure and function. We suggest that the endogenous chromosomal location imparts stability to this Polycomb domain. To test this possibility, a 79-kbentransgene was inserted into other chromosomal locations. This transgene is functional and forms a Polycomb domain. The spreading of the H3K27me3 repressive mark, characteristic of PcG domains, varies depending on the chromatin context of the transgene. Unlike at the endogenous locus, deletion of the strong, constitutive PREs from the transgene leads to both loss- and gain-of function phenotypes, demonstrating the important role of these regulatory elements. Our data show that chromatin context plays an important role in Polycomb domain structure and function.

scholarly journals dom2vec: Capturing domain structure and function using self-supervision on protein domain architectures

2020 ◽  
Author(s):  
Damianos P. Melidis ◽  
Brandon Malone ◽  
Wolfgang Nejdl
Keyword(s):  
Domain Structure ◽  
Language Processing ◽  
Performance Comparison ◽  
Structure And Function ◽  
Protein Domain ◽  
Linguistic Features ◽  
Enzymatic Function ◽  
Protein Prediction ◽  
And Function

Abstract Background: Word embedding approaches have revolutionized natural language processing (NLP) research. These approaches aim to map words to a low-dimensional vector space, in which words with similar linguistic features cluster together. Embedding-based methods have also been developed for proteins, where words are amino acids and sentences are proteins. The learned embeddings have been evaluated qualitatively, via visual inspection of the embedding space and extrinsically, via performance comparison on downstream protein prediction tasks. However, these sequence embeddings have the caveat that biological metadata do not exist for each amino acid, in order to measure the quality of each unique learned embedding vector. Results: Here, we present dom2vec, an approach for learning protein domain embeddings using word2vec on InterPro annotations. In contrast to sequence embeddings, biological metadata do exist for protein domains, related to each domain separately. Therefore, we present four intrinsic evaluation strategies to quantitatively assess the quality of the learned embedding space. To perform a reliable evaluation in terms of biology knowledge, we selected the metadata related to the most distinctive biological characteristics of domains. These are the structure, enzymatic and molecular function of a given domain. Notably, dom2vec obtains adequate level of performance in the intrinsic assessment, therefore we can draw an analogy between the local linguistic features in natural languages and the domain structure and function information in domain architectures. Moreover, we demonstrate the dom2vec applicability on protein prediction tasks, by comparing it with state-of-the-art sequence embeddings in three downstream tasks. We show that dom2vec outperform sequence embeddings for toxin and enzymatic function prediction and is comparable with sequence embeddings in cellular location prediction. Conclusions: We report that the application of word2vec on InterPro annotations produces domain embeddings with two significant advantages over sequence embeddings. First, each unique dom2vec vector can be quantitatively evaluated towards its available structure and function metadata. Second, the produced embeddings can outperform the sequence embeddings for a subset of downstream tasks. Overall, dom2vec embeddings are able to capture the most important biological properties of domains and surpass sequence embeddings for a subset of prediction tasks.

scholarly journals STAS Domain Structure and Function

2011 ◽  
Vol 28 (3) ◽  
pp. 407-422 ◽  
Author(s):  
Alok K. Sharma ◽  
Alan C. Rigby ◽  
Seth L. Alper
Keyword(s):  
Domain Structure ◽  
Structure And Function ◽  
Stas Domain ◽  
And Function

scholarly journals LRIG1 Extracellular Domain: Structure and Function Analysis

2015 ◽  
Vol 427 (10) ◽  
pp. 1934-1948 ◽  
Author(s):  
Yibin Xu ◽  
Priscilla Soo ◽  
Francesca Walker ◽  
Hui Hua Zhang ◽  
Nicholas Redpath ◽  
...  
Keyword(s):  
Domain Structure ◽  
Function Analysis ◽  
Extracellular Domain ◽  
Structure And Function ◽  
And Function

scholarly journals IMGT® and 30 Years of Immunoinformatics Insight in Antibody V and C Domain Structure and Function

Antibodies ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 29 ◽  
Author(s):  
Lefranc ◽  
Lefranc
Keyword(s):  
Domain Structure ◽  
High Throughput Sequencing ◽  
Genome Mapping ◽  
Three Dimensional ◽  
Cell Receptor ◽  
Structure And Function ◽  
Antibody Engineering ◽  
New Science ◽  
And Function ◽  
First Time

At the 10th Human Genome Mapping (HGM10) Workshop, in New Haven, for the first time, immunoglobulin (IG) or antibody and T cell receptor (TR) variable (V), diversity (D), joining (J), and constant (C) genes were officially recognized as ‘genes’, as were the conventional genes. Under these HGM auspices, IMGT®, the international ImMunoGeneTics information system® (http://www.imgt.org), was created in June 1989 at Montpellier (University of Montpellier and CNRS). The creation of IMGT® marked the birth of immunoinformatics, a new science, at the interface between immunogenetics and bioinformatics. The accuracy and the consistency between genes and alleles, sequences, and three-dimensional (3D) structures are based on the IMGT Scientific chart rules generated from the IMGT-ONTOLOGY axioms and concepts: IMGT standardized keywords (IDENTIFICATION), IMGT gene and allele nomenclature (CLASSIFICATION), IMGT standardized labels (DESCRIPTION), IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION). These concepts provide IMGT® immunoinformatics insights for antibody V and C domain structure and function, used for the standardized description in IMGT® web resources, databases and tools, immune repertoires analysis, single cell and/or high-throughput sequencing (HTS, NGS), antibody humanization, and antibody engineering in relation with effector properties.

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