Premium
Scalable remote homology detection and fold recognition in massive protein networks
Author(s) -
Petegrosso Raphael,
Li Zhuliu,
Srour Molly A.,
Saad Yousef,
Zhang Wei,
Kuang Rui
Publication year - 2019
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25669
Subject(s) - casp , computer science , scalability , massively parallel , protein sequencing , threading (protein sequence) , smith–waterman algorithm , pairwise comparison , theoretical computer science , computational biology , artificial intelligence , protein structure , protein structure prediction , sequence alignment , parallel computing , biology , gene , peptide sequence , genetics , database , biochemistry
Abstract The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low‐rank kernel approximation (LP‐LOKA) for searching massively large protein networks. LP‐LOKA propagates initial protein similarities in a low‐rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed‐memory using message‐passing interface and Apache‐Hadoop/Spark on cloud, LP‐LOKA can search protein networks with one million proteins or more. In the experiments on Swiss‐Prot/ADDA/CASP data, LP‐LOKA significantly improved protein ranking over the widely used HMM‐HMM or profile‐sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP‐LOKA is both scalable and accurate for searching massively large protein networks.