Computational biology for target discovery and characterization: a feasibility study in protein-protein interaction detection

Zhou, C ; Zemla, A

关键词: ALGORITHMS;    BIOLOGY;    CONSTRUCTION;    DETECTION;    LAWRENCE LIVERMORE NATIONAL LABORATORY;    PATHOGENS;    PROTEINS;    TARGETS;    VIRULENCE;   

DOI  :  10.2172/948981
RP-ID  :  LLNL-TR-410910
PID  :  OSTI ID: 948981
Others  :  TRN: US200909%%379

学科分类：社会科学、人文和艺术（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

In this work we developed new code for detecting putative multi-domain protein-protein interactions for a small network of bacterial pathogen proteins, and determined how structure-driven domain-fusion (DF) methods should be scaled up for whole-proteome analysis. Protein-protein interactions are of great interest in structural biology and are important for understanding the biology of pathogens. The ability to predict protein-protein interactions provides a means for development of anti-microbials that may interfer with key processes in pathogenicity. The function of a protein-protein complex can be elucidated through knowledge of its structure. The overall goal of this project was to determine the feasibility of extending current LLNL capabilities to produce a high-throughput systems bio-informatics capability for identification and characterization of putative interacting protein partners within known or suspected small protein networks. We extended an existing LLNL methodology for identification of putative protein-protein interacting partners (Chakicherla et al (in review)) by writing a new code to identify multi-domain-fusion linkages (3 or more per complex). We applied these codes to the proteins in the Yersinia pestis quorum sensing network, known as the lsr operon, which comprises a virulence mechanism in this pathogen. We determined that efficient application of our computational algorithms in high-throughput for detection of putative protein-protein complexes genome wide would require pre-computation of PDB domains and construction of a domain-domain association database.