6-1. Summary
In this thesis, we presented the "3D-domain interologs mapping" and "protein complex family" to construct the structure resolved PPI networks across multiple organisms.
"3d-domain interolos mapping" is a concept for efficiently enlarging protein interactions annotated through the homologous PPIs with residue-based binding models. We verified the structure resolved PPI networks on Gene Ontology annotations36 and the architecture of topology (i.e. scale-free network properties). In addition, we also provide the consensus proteins across three networks based on "3D-domain interologs mapping". These consensus proteins are highly related to the essential genes and disease related proteins. We believe that structure resolved PPI networks would provide the insight for understanding the mechanism of biological processes within a given PPI network. In summary, the major contributions of this study are listed as the following:
1. We proposed several new concepts, including "3D-domain interologs mapping" and
"protein complex family", to study the evolution of PPIs and protein complexes across multiple species. A group of PPIs are regarded as a PPI family when they meet the following criteria: (1) The proteins of the PPIs are homologous proteins, respectively; (2) The interactions of PPIs share the similar binding model based on the structure templates.
In addition, a group of protein complexes are regarded as a protein complex family when they meet the two criteria and an additional criterion: the protein complexes share the similar complex similarity. More importantly, these two concepts provide a new way to efficiently enlarge the PPIs and protein complexes annotated with residue-based binding models.
2. We developed a database, namely 3D-interologs, records the evolution of protein-protein interactions database across multiple species derived from “3D-domain interolog mapping”
and a template-based scoring function. We have inferred 173,294 homologous protein-protein interactions by using 1,895 three-dimensional (3D) structure heterodimers to search the UniProt database (4,826,134 protein sequences). For a protein-protein interaction, the 3D-interologs database shows interacting domains and binding models derived from structure template. More importantly, this database provides the evolution of PPI by exploring its PPI family across multiple species.
3. We developed a web server, namely PCFamily, for identifying homologous complexes and inferring conserved domains and GO terms from protein complex families. PCFamily is the first server to provide homologous complexes in multiple species; graphic visualization of the complex topology and detailed atomic residue-residue interactions;
interface alignments; conservations of GO terms and domain compositions. We believe that the server is able to provide valuable insights for determining functional modules of biological networks across multiple species.
4. Based on the two concepts, we were able to construct the structure resolved PPI networks in H. sapiens, M. musculus, and D. rerio. In each structure resolved network, the PPIs with atomic residue-based binding models in the derived structure resolved network achieved highly agreement with Gene Ontology similarities. In addition, our derived networks can be used to observe the consensus proteins and modules derived from the multiple network alignment of H. sapiens, M. musculus, and D. rerio. These consensus proteins are often the essential genes and play key roles in the architecture of these networks. More importantly, our results demonstrate that the structure resolved PPI networks would provide valuable insights into understanding the mechanism of biological processes (e.g. cancer, cardiovascular-related diseases, and complement and coagulation
pathway) across multiple organisms.
6-2. Discussion and future work
According to the characteristics of "3D-domain interologs mapping" and "protein complex family", the interactome behavior, we discussed here, is focused on the conserved proteins and PPIs which are the members of the PPI and protein complex families. In this thesis, we used our concept to studying the evolution of these PPIs and protein complexes.
Therefore, we only discuss the conservation and difference of these consensus pathways across multiple organisms. However, the organism-specific proteins and PPIs usually play an important role during the organism evolution. This issue should be the next important issue for our studies.
Our structural networks can annotate and infer the cell behaviors of a new determined (or seldom-studied) species (e.g. zebrafish), by mapping some well-studied species. However, the construction of our structurally resolved PPI networks largely relies on the availability of 3D-crystal structures, which limits the coverage of our network. But, we believe that the rapid growth of PDB providing more 3D-crystal information and our methods can be readily applied to uncover potential molecular mechanisms whose structural information is currently missing.
In addition, our methods should also be considering these high-quality experimental PPIs with possible domain annotations. Prof. Yang Lab has already provided the sequence-based PPI family for annotating and studying PPIs across multiple organisms with non-structure information. Although the accuracy of method for PPI annotation is less than "3D-domain interologs mapping", the sequence-based PPI family has more coverage to explore the non-well-known organisms. In the future work, we could carefully utilize sequence-based PPI family with high-quality experimental PPIs to enlarging the coverage of PPIs and provide a
more complete PPI network for understanding the mechanism of cell behaviors.
However, dynamic architecture of the protein interaction network has an important role in the regulation of cell behavior. Understanding the functional organization of protein interaction networks is the most important issue for understanding the principles of cellular behavior.
More importantly, it also provides a way for understanding the diseases where cellular behavior is miss-regulated. Currently, most of these studies have considered the protein interaction networks without taking into account the dynamic nature of protein expression, which is essential for a proper representation of biological networks.
In current state, we have already been able to construct the structure resolved PPI networks in multiple organisms. We also provide the consensus proteins and PPIs in these networks. According to our results, the structure resolved PPI networks derived from the PPI family would provide the insight for understanding the mechanism of biological processes within a given PPI network. To further investigate the behavior of PPI network within a given cell, gene expression data would provide an aspect of in-depth understanding of the dynamic organization of the PPI network and its role in the regulation of cellular processes. For example, the Connectivity Map (also known as cmap) provided by Lamb, J. et al. is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules and simple pattern-matching algorithms that together enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes 37.
Therefore, we will combine the gene expression data into the PPI network. We will try to illustrate the behavior of PPI networks under different cell types and different conditions.
Because the Connectivity Map could provide the up-regulated and down-regulated proteins of given drugs and diseases, combining these data with our structure resolved PPI networks should be able to explain the mechanism of relationship between the drugs, genes and diseases.
List of publications
Journal paper
1. C.-Y Lin, Y.-W. Lin, S.-W. Yu, Y.-S. Lo, and J.-M. Yang*, "MoNetFamily: a web server to infer homologous modules and module-module interaction networks in vertebrates,"
Nucleic Acids Research, W263-W270, 2012.
2. S.-C. Hsu, C.-P. Chang, C.-Y. Tsai, S.-H. Hsieh, B.A. Wu-Hsieh, Y.-S. Lo, and J.-M. Yang,
"Steric recognition of TCR contact residues is required to map mutant epitopes by immunoinformatical programmes," Immunology, 139-152, 2012.
3. I-H. Liu, Y.-S. Lo, and J.-M. Yang*, "Template-based Scoring Functions for Visualizing Biological Insights of H-2Kb–peptide–TCR Complexes," International Journal of Data Mining and Bioinformatics, 2012, in press.
4. I-H. Liu, Y.-S. Lo, and J.-M. Yang*, "PAComplex: a web server to infer peptide antigen families and binding models from TCR–pMHC complexes," Nucleic Acids Research, W254-W260, 2011.
5. Y.-S. Lo, C.-Y Lin, and J.-M. Yang*, "PCFamily: a web server for searching homologous protein complexes," Nucleic Acids Research, W516-W522, 2010.
6. Y.-S. Lo, Y.-C. Chen, and J.-M. Yang*, "3D-interologs: An evolution database of physical protein-protein interactions across multiple genomes," BMC Genomics, (Suppl 3):S7, 2010.
7. C.-C. Chen, C.-Y. Lin, Y.-S. Lo, and J.-M. Yang*, "PPISearch: a web server for searching homologous protein-protein interactions across multiple species," Nucleic Acids Research, W369-W375, 2009.
8. Y.-C. Chen, Y.-S. Lo, W.-C. Hsu, and J.-M. Yang*, "3D-partner: a web server to infer interacting partners and binding models, " Nucleic Acids Research, W561-W567, 2007.
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