Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

Ding, Ziyun; Kihara, Daisuke

doi:10.1002/cpps.62

Cited by 55 publications

(45 citation statements)

References 220 publications

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“…POLA1 is linked to MCM4 protein expression. Gene coexpression analysis can provide insight into functional associations because interacting protein pairs tend to behave similarly across different expression conditions (19). This approach indicated that POLA1 expression is coregulated with genes encoding other subunits of the Pol-α/primase complex, along with genes in the DNA damage response and cell cycle regulation, including MCM4 (summary data presented in Table 3, entire analysis available in Supplemental Table 1).…”

Section: Resultsmentioning

confidence: 99%

NK cell defects in X-linked pigmentary reticulate disorder

Starokadomskyy¹,

Wilton²,

Krzewski³

et al. 2019

JCI Insight

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Section: Resultsmentioning

confidence: 99%

NK cell defects in X-linked pigmentary reticulate disorder

Starokadomskyy¹,

Wilton²,

Krzewski³

et al. 2019

JCI Insight

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“…For sequence-based pair-wise PPI prediction (37), the amino acid sequences of Cyanothece 51142 proteins were downloaded from CyanoBase (http://genome.annotation.jp/CyanoBase) (38). The experimental results contained GeneBank protein IDs starting with “gi” and were converted into RefSeq ID following instructions on the GenBank webpage and the UniProt database (39).…”

Section: Experimental Methodsmentioning

confidence: 99%

Analysis of Protein Complexes in the Unicellular Cyanobacterium Cyanothece ATCC 51142

et al. 2018

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The unicellular cyanobacterium Cyanothece ATCC 51142 is capable of oxygenic photosynthesis and biological N fixation (BNF), a process highly sensitive to oxygen. Previous work has focused on determining protein expression levels under different growth conditions. A major gap of our knowledge is an understanding on how these expressed proteins are assembled into complexes and organized into metabolic pathways, an area that has not been thoroughly investigated. Here, we combined size-exclusion chromatography (SEC) with label-free quantitative mass spectrometry (MS) and bioinformatics to characterize many protein complexes from Cyanothece 51142 cells grown under a 12 h light-dark cycle. We identified 1386 proteins in duplicate biological replicates, and 64% of those proteins were identified as putative complexes. Pairwise computational prediction of protein-protein interaction (PPI) identified 74 822 putative interactions, of which 2337 interactions were highly correlated with published protein coexpressions. Many sequential glycolytic and TCA cycle enzymes were identified as putative complexes. We also identified many membrane complexes that contain cytoplasmic domains. Subunits of NDH-1 complex eluted in a fraction with an approximate mass of ∼669 kDa, and subunits composition revealed coexistence of distinct forms of NDH-1 complex subunits responsible for respiration, electron flow, and CO uptake. The complex form of the phycocyanin beta subunit was nonphosphorylated, and the monomer form was phosphorylated at Ser20, suggesting phosphorylation-dependent deoligomerization of the phycocyanin beta subunit. This study provides an analytical platform for future studies to reveal how these complexes assemble and disassemble as a function of diurnal and circadian rhythms.

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“…ML works on numerical representation of proteins; hence physical and chemical descriptors were developed to represent proteins [ 8 , 9 , 10 ] and used to predict PPI [ 11 , 12 ]. Other descriptors, such as proteins sequence composition [ 13 , 14 ], genomic data [ 15 , 16 ], and protein three-dimensional (3D) structures [ 17 , 18 ], among others [ 19 , 20 ], were described to represent proteins to predict PPI.…”

Section: Introductionmentioning

confidence: 99%

Protein–Protein Interactions Efficiently Modeled by Residue Cluster Classes

Velez

Fontove²,

Río

2020

IJMS

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Predicting protein–protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm–parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96–99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.

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Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

Cited by 55 publications

References 220 publications

NK cell defects in X-linked pigmentary reticulate disorder

NK cell defects in X-linked pigmentary reticulate disorder

Analysis of Protein Complexes in the Unicellular Cyanobacterium Cyanothece ATCC 51142

Protein–Protein Interactions Efficiently Modeled by Residue Cluster Classes

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