PSIbase: a database of Protein Structural Interactome map (PSIMAP)

Gong, Sungsam; Yoon, Gi-Seok; Jang, Insoo; Bolser, Dan; Dafas, Panos; Schroeder, Michael; Choi, Hansol; Cho, Yoobok; Han, Kyungsook; Lee, Sung-Hoon; Choi, Ho-Jin; Lappé, Michael; Holm, Liisa; Kim, Sang Soo; Oh, Donghoon; Bhak, Jonghwa

doi:10.1093/bioinformatics/bti366

Cited by 89 publications

(52 citation statements)

References 10 publications

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“…By extracting the protein sequences of every protomer from each complex and searching for it against the yeast genome by using BLAST (48) (cut-off, E Յ 1 ϫ 10 Ϫ10 , fraction of conserved residues Ն35%), we were able to identify and make use of protein complexes determined in other species that are structural homologs of those found in S. cerevisiae. Actual physical interactions between protomers within the complexes were identified by computational analysis of the crystal structure by using an empirically equivalent algorithm to the full atom contact method used by Gong et al (49); an interaction between two protomers was defined as the occurrence of at least five instances in which C␣ atoms within the chains come within 7.5 Å of each other. This set was included in the ''true positives'' set of interacting proteins.…”

Section: Methodsmentioning

confidence: 99%

Specificity in protein interactions and its relationship with sequence diversity and coevolution

Hakes

Lovell

Oliver

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

116

125

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Studies of interacting proteins have found correlated evolution of the sequences of binding partners, apparently as a result of compensating mutations to maintain specificity (i.e., molecular coevolution). Here, we analyze the coevolution of interacting proteins in yeast and demonstrate correlated evolution of binding partners in eukaryotes. Detailed investigation of this apparent coevolution, focusing on the proteins' surface and binding interface, surprisingly leads to no improvement in the correlation. We conclude that true coevolution, as characterized by compensatory mutations between binding partners, is unlikely to be chiefly responsible for the apparent correlated evolution. We postulate that the correlation between sequence alignments is simply due to interacting proteins being subject to similar constraints on their evolutionary rate. Because gene expression has a strong influence on evolutionary rate, and interacting proteins will tend to have similar levels of expression, we investigated this particular constraint. We found that the absolute expression level outperformed correlated evolution for predicting interacting protein partners. A correlation between sequence alignments could also be identified not only between pairs of proteins that physically interact but also between those that are merely functionally related (i.e., within the same protein complex). This indicates that the observed correlated evolution of interacting proteins is due to similar constraints on evolutionary rate and not coevolution.evolutionary constraints ͉ gene expression ͉ binding interfaces

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Section: Methodsmentioning

confidence: 99%

Specificity in protein interactions and its relationship with sequence diversity and coevolution

Hakes

Lovell

Oliver

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

116

125

View full text Add to dashboard Cite

show abstract

“…Another, relatively inexpensive, way to predict protein-protein interactions does not include wet lab analysis, using instead a variety of computational approaches. These approaches can complement experimental wet lab techniques and are often supported by either the hypothesis of protein co-evolution [Tan et al 2004, Tillier et al 2006, Izarzugaza et al 2006], structural similarities [Gong et al 2005, Ogmen et al 2005 or amino-acids sequence conservation [Pitre et al 2006].…”

Section: Current Computational Approaches For Predicting Protein-protmentioning

confidence: 99%

“…Three of them, supported by the protein co-evolution hypothesis, are: TSEMA [Izarzugaza et al 2006], ADVICE [Tan et al 2004], Codep [Tillier et al 2006]. The other three, supported by datasets of verified interactions, are: PIPE [Pitre et al 2006], PSIbase [Gong et al 2005], and PRISM [Ogmen et al 2005]. In the next Sections, we describe each one of these two types of methods.…”

mentioning

confidence: 99%

Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings

Iacucci¹,

Xavier‐de‐Souza²,

Moreau³

2012

Protein-Protein Interactions - Computational and Experimental Tools

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“…There are however smaller databases that contain information regarding the different aspects of protein-DNA interaction. The NDB, PSIBASE and AANT databases contain information obtained from DNA protein complexes deposited at the PDB [311][312][313]. NPInter also has information about interactions between protein and non coding RNA in E. coli, S. cerevisiae, C. elegans, D. melanogaster, M. musculus, and H. sapiens [314].…”

Section: Physical Interactionsmentioning

confidence: 99%

Integrating Bioinformatics and Computational Biology: Perspectives and Possibilities for In Silico Network Reconstruction in Molecular Systems Biology

Alves

Vilaprinyó²,

Sorribas³

2008

CBIO

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Abstract:There is a flood of molecular data about many aspects of cellular functioning. This data ranges from sequence and structural data to gene and protein regulation data, including time dependent changes in the concentration. Integration of the different datasets through computational methods is required to extract biological information that is relevant from a systems biology perspective.In this paper we discuss how different computational tools and methods can be made to work together integrating different types of data, mining these data for biological information, and assisting in pathway reconstruction and biological hypotheses generation. We review the recent body of literature where such integrative approaches are used and discuss automation of data integration and model building to generate testable biological hypotheses. We analyze issues regarding the design of such automated tools and discuss what limitations and pitfalls can be foreseen for the automation and what solutions can computer science and biologists provide to overcome them.

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PSIbase: a database of Protein Structural Interactome map (PSIMAP)

Abstract: http://psimap.org and http://psibase.kaist.ac.kr/

Cited by 89 publications

References 10 publications

Specificity in protein interactions and its relationship with sequence diversity and coevolution

Specificity in protein interactions and its relationship with sequence diversity and coevolution

Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings

Integrating Bioinformatics and Computational Biology: Perspectives and Possibilities for In Silico Network Reconstruction in Molecular Systems Biology

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