PPISearch: a web server for searching homologous protein–protein interactions across multiple species

Chen, Chun Chen; Lin, Chun Yuan; Lo, Yu Shu; Yang, Jinn-Moon

doi:10.1093/nar/gkp309

Cited by 35 publications

(49 citation statements)

References 22 publications

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“…To overcome these limitations, bioinformatics methods are expected to be useful for identifying PPIs in functional proteomics. Various computational methods have been proposed to predict PPIs, which include genomic context-based methods [13][14][15][16][17], structure-based methods [18][19][20][21], and sequence-based methods [22][23][24][25][26][27][28][29][30][31][32]. Genomic context-based methods such as gene-cluster and gene-neighbor methods are based on the search of pairs of genes that show a correlated position or behavior; these genes are assumed to encode proteins that interact with each other [13].…”

Section: Introductionmentioning

confidence: 99%

“…They suggested that sequence similarity above a certain threshold might be a reliable measure for identifying PPIs. Recently, several web servers, such as PPI-Search [30] and BIPS [31], have also been constructed for searching for homologous PPIs based on sequences. These services may assist in the identification of pairs of proteins that potentially interact.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2017

RJLBPCS

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Protein-protein interactions (PPIs) play an essential role in various biological processes. A range of computational methods have been proposed to predict PPIs from protein sequences. Among these, homology-based methods and machine-learning methods have been widely used. However, to the best of our knowledge, these two methods have not been compared using the same dataset. Thus in this study, we have developed both homology-based and machine-learning methods to predict PPIs from amino-acid sequences and compared the prediction results. In the homology-based method, BLASTP search was used to identify sequence homology. Regarding the machine-learning methods, two popular methods, support vector machine and random forest, as well as six different protein features, were employed to build classifiers. We collected the PPI pairs with high-confidence scores from HitPredict4 to build the positive dataset and we built the negative dataset from the Negatome 2.0 database, in which non-interacting pairs were verified by experiments and 3D structure analysis. Our results show that machine-learning methods achieved better performance than homology-based method but there are many PPIs that are predicted only by the homology-based method. The integration of the two methods is expected to enhance the performance.KEYWORDS: protein-protein interactions, protein-protein interaction site prediction, support vector machine, machine learning, homology search.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2017

RJLBPCS

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“…This deductive inference has been applied to the identification of PPIs, on the assumption that homologous proteins share similar interaction patterns as well as similar functions [18]. A pair of interacting proteins in one species and their respective orthologs in another species, which are also known to interact with each other, have been traditionally defined as interaction-orthologs (interologs) [19,20].…”

Section: Introductionmentioning

confidence: 99%

Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators

Murakami

Mizuguchi

2014

BMC Bioinformatics

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BackgroundIdentification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions. However, experimental identification of the complete set of PPIs in a cell/organism (“an interactome”) is still a difficult task. To circumvent limitations of current high-throughput experimental techniques, it is necessary to develop high-performance computational methods for predicting PPIs.ResultsIn this article, we propose a new computational method to predict interaction between a given pair of protein sequences using features derived from known homologous PPIs. The proposed method is capable of predicting interaction between two proteins (of unknown structure) using Averaged One-Dependence Estimators (AODE) and three features calculated for the protein pair: (a) sequence similarities to a known interacting protein pair (FSeq), (b) statistical propensities of domain pairs observed in interacting proteins (FDom) and (c) a sum of edge weights along the shortest path between homologous proteins in a PPI network (FNet). Feature vectors were defined to lie in a half-space of the symmetrical high-dimensional feature space to make them independent of the protein order. The predictability of the method was assessed by a 10-fold cross validation on a recently created human PPI dataset with randomly sampled negative data, and the best model achieved an Area Under the Curve of 0.79 (pAUC0.5% = 0.16). In addition, the AODE trained on all three features (named PSOPIA) showed better prediction performance on a separate independent data set than a recently reported homology-based method.ConclusionsOur results suggest that FNet, a feature representing proximity in a known PPI network between two proteins that are homologous to a target protein pair, contributes to the prediction of whether the target proteins interact or not. PSOPIA will help identify novel PPIs and estimate complete PPI networks. The method proposed in this article is freely available on the web at http://mizuguchilab.org/PSOPIA.

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“…The consensus anchor, the subpocket–moiety interactions with statistical significance sharing by some particular protein kinases, can be regarded as a ‘hot spot’ that represents the conserved binding environments involved in inhibitor bindings and biological functions. As a result, a group of KIIs with consensus anchors can constitute a kinase-inhibitor family (KIF), which is analogous to a protein sequence family (18,19), a structure family (20) and a protein–protein interaction family (21). …”

Section: Introductionmentioning

confidence: 99%

KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms

Chiu¹,

Lin²,

Huang³

et al. 2012

Nucleic Acids Research

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Kinases play central roles in signaling pathways and are promising therapeutic targets for many diseases. Designing selective kinase inhibitors is an emergent and challenging task, because kinases share an evolutionary conserved ATP-binding site. KIDFamMap (http://gemdock.life.nctu.edu.tw/KIDFamMap/) is the first database to explore kinase-inhibitor families (KIFs) and kinase-inhibitor-disease (KID) relationships for kinase inhibitor selectivity and mechanisms. This database includes 1208 KIFs, 962 KIDs, 55 603 kinase-inhibitor interactions (KIIs), 35 788 kinase inhibitors, 399 human protein kinases, 339 diseases and 638 disease allelic variants. Here, a KIF can be defined as follows: (i) the kinases in the KIF with significant sequence similarity, (ii) the inhibitors in the KIF with significant topology similarity and (iii) the KIIs in the KIF with significant interaction similarity. The KIIs within a KIF are often conserved on some consensus KIDFamMap anchors, which represent conserved interactions between the kinase subsites and consensus moieties of their inhibitors. Our experimental results reveal that the members of a KIF often possess similar inhibition profiles. The KIDFamMap anchors can reflect kinase conformations types, kinase functions and kinase inhibitor selectivity. We believe that KIDFamMap provides biological insights into kinase inhibitor selectivity and binding mechanisms.

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PPISearch: a web server for searching homologous protein–protein interactions across multiple species

Cited by 35 publications

References 22 publications

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Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators

KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms

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