Computational Methods for Predicting Domain–Domain Interactions

Physical interactions between proteins are important for many cellular functions. Since protein-protein interactions are mediated via their interaction sites, identifying these interaction sites can therefore help to discover genome-scale protein interaction map, thereby leading to a better understanding of the organization of living cell. To date, the experimentally solved protein interaction sites constitute only a tiny proportion among the whole population due to the high cost and low-throughput of currently available techniques. Computational methods, including many biological data mining methods, are considered as the major approaches in discovering protein interaction sites in practical applications. This chapter reviews both traditional and recent computational methods such as protein-protein docking and motif discovery, as well as new methods on machine learning approaches, for example, interaction classification, domain-domain interactions, and binding motif pair discovery.

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AN INTEGRATIVE DOMAIN-BASED APPROACH TO PREDICTING PROTEIN–PROTEIN INTERACTIONS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720008003874 ◽

2008 ◽

Vol 06 (06) ◽

pp. 1115-1132 ◽

Cited By ~ 11

Author(s):

THANH-PHUONG NGUYEN ◽

TU-BAO HO

Keyword(s):

Computational Methods ◽

Protein Interactions ◽

Inductive Logic ◽

High Sensitivity ◽

Protein Domain ◽

Protein Protein Interactions ◽

Reciprocal Relationships ◽

Multiple Data ◽

Domain Interactions ◽

Almost All

Protein–protein interactions (PPIs) are intrinsic to almost all cellular processes. Different computational methods offer new chances to study PPIs. To predict PPIs, while the integrative methods use multiple data sources instead of a single source, the domain-based methods often use only protein domain features. Integration of both protein domain features and genomic/proteomic features from multiple databases can more effectively predict PPIs. Moreover, it allows discovering the reciprocal relationships between PPIs and biological features of their interacting partners. We developed a novel integrative domain-based method for predicting PPIs using inductive logic programming (ILP). Two principal domain features used were domain fusions and domain–domain interactions (DDIs). Various relevant features of proteins were exploited from five popular genomic and proteomic databases. By integrating these features, we constructed biologically significant ILP background knowledge of more than 278,000 ground facts. The experimental results through multiple 10-fold cross-validations demonstrated that our method predicts PPIs better than other computational methods in terms of typical performance measures. The proposed ILP framework can be applied to predict DDIs with high sensitivity and specificity. The induced ILP rules gave us many interesting, biologically reciprocal relationships among PPIs, protein domains, and PPI-related genomic/proteomic features. Supplementary material is available at .

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Investigation of ferroelectrics using conventional and in situ electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100170669 ◽

1994 ◽

Vol 52 ◽

pp. 586-587

Author(s):

V. Saikumar ◽

H. M. Chan ◽

M. P. Harmer

Keyword(s):

Nonvolatile Memory ◽

Ferroelectric Materials ◽

Gate Insulator ◽

Sensors And Actuators ◽

In Situ Electron Microscopy ◽

Ferroelectric Domains ◽

Domain Boundaries ◽

Domain Interactions ◽

Memory Applications

In recent years, there has been a growing interest in the application of ferroelectric thin films for nonvolatile memory applications and as a gate insulator in DRAM structures. In addition, bulk ferroelectric materials are also widely used as components in electronic circuits and find numerous applications in sensors and actuators. To a large extent, the performance of ferroelectric materials are governed by the ferroelectric domains (with dimensions in the micron to sub-micron range) and the switching of domains in the presence of an applied field. Conventional TEM studies of ferroelectric domains structures, in conjunction with in-situ studies of the domain interactions can aid in explaining the behavior of ferroelectric materials, while providing some answers to the mechanisms and processes that influence the performance of ferroelectric materials. A few examples from bulk and thin film ferroelectric materials studied using the TEM are discussed below.Figure 1 shows micrographs of ferroelectric domains obtained from undoped and Fe-doped BaTiO3 single crystals. The domain boundaries have been identified as 90° domains with the boundaries parallel to <011>.

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