Associating Protein Domains with Biological Functions: A Tripartite Network Approach

被引:2
作者
Rojano, Elena [1 ]
Richard Perkins, James [2 ]
Sillitoe, Ian [3 ]
Orengo, Christine [3 ]
Garcia Ranea, Juan Antonio [1 ,2 ]
Seoane, Pedro [2 ]
机构
[1] Univ Malaga, Dept Mol Biol & Biochem, Bulevar Louis Pasteur 31, Malaga 29010, Spain
[2] ISCIII, CIBER Enfermedades Raras, Av Monforte de Lemos 3-5,Pabellon 11,Planta 0, Madrid 28029, Spain
[3] UCL, Dept Struct & Mol Biol, Gower St, London WC1E 6BT, England
来源
BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2019), PT II | 2019年 / 11466卷
基金
英国生物技术与生命科学研究理事会;
关键词
Network analysis; FunFam; CATH; Protein structure; Structural domains; Functional annotation; GENE; CATH; ANNOTATIONS; RESOURCE;
D O I
10.1007/978-3-030-17935-9_15
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Protein domains are key determinants of protein function. However, a large number of domains have no recorded functional annotation. These domains of unknown function (DUFs) are a recognised problem and efforts have been made to remedy this situation, including the use of data such as structural and sequence similarity and annotation data such as that of Gene Ontology (GO) and The Enzyme Commission. Here, we present a new approach based on tripartite network analysis to assign functional terms to DUFs. We combine functional annotation at the protein level, taken from GO, KEGG, Reactome and UniPathway, with structural domain annotation, taken from the CATH-Gene3D resource. We validate our method using 10-fold cross-validation and find it performs well when assigning annotation from the UniPathway, Reactome and GO resources, but less well for KEGG. We also explored using a finer functional subclassification of CATH superfamilies (FunFams) but these families were found to be too specific in this context.
引用
收藏
页码:155 / 164
页数:10
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