Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins

Lin, Tzu Wen; Wu, Jian Hong; Chang, Darby Tien Hao

doi:10.1371/journal.pone.0075940

Cited by 17 publications

(12 citation statements)

References 20 publications

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“…The one residue change (T399I), though it is not present on or near active site, can hamper the signaling by altering the local structure, this behavior can be implicated to account the influence of other residues substitution ( Table 1 ). So far, the sequence-structure and phylogenetic relationship are the most plausible and strongly correlated factor in these analyses 45 46 . In our study, we selected two species each for the agonistic (horse and hamster) and antagonistic (human and murine) groups to strengthen the in silico analysis 23 .…”

Section: Discussionmentioning

confidence: 99%

Insights into the species-specific TLR4 signaling mechanism in response to Rhodobacter sphaeroides lipid A detection

Anwar

Suresh

Shah

et al. 2015

Sci Rep

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TLR4 in complex with MD2 senses the presence of lipid A (LA) and initiates a signaling cascade that curb the infection. This complex is evolutionarily conserved and can initiate the immune system in response to a variety of LAs. In this study, molecular dynamics simulation (25 ns) was performed to elucidate the differential behavior of TLR4/MD2 complex in response to Rhodobacter sphaeroides lipid A (RsLA). Penta-acyl chain-containing RsLA is at the verge of agonist (6 acyl-chains) and antagonist (4 acyl-chains) structure, and activates the TLR4 pathway in horses and hamsters, while inhibiting in humans and murine. In the time-evolved coordinates, the promising factors that dictated the differential response included the local and global mobility pattern of complexes, solvent-accessible surface area of ligand, and surface charge distributions of TLR4 and MD2. We showed that the GlcN1-GlcN2 backbone acquires agonist (3FXI)-like configurations in horses and hamsters, while acquiring antagonist (2E59)-like configurations in humans and murine systems. Moreover, analysis of F126 behavior in the MD2 F126 loop (amino acids 123–129) and loop EF (81–89) suggested that certain sequence variations also contribute to species-specific response. This study underlines the TLR4 signaling mechanism and provides new therapeutic opportunities.

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Section: Discussionmentioning

confidence: 99%

Insights into the species-specific TLR4 signaling mechanism in response to Rhodobacter sphaeroides lipid A detection

Anwar

Suresh

Shah

et al. 2015

Sci Rep

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“…Phylogenetic-profile-based methods conceded satisfactory performance only on prokaryotes but not on eukaryotes [56]. …”

Section: Classification Of Ppi Detection Methodsmentioning

confidence: 99%

Protein-Protein Interaction Detection: Methods and Analysis

Rao

Srinivas

Sujini

et al. 2014

International Journal of Proteomics

613

380

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Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

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“…Therefore, more and more scholars use computational methods to predict protein-protein interactions. At present, there are many computational methods based on genome information, genetic evolution (Tsoka and Ouzounis, 2000;Chen et al, 2006;Lin et al, 2013) and protein structure (Planas-Iglesias et al, 2013;Zhao et al, 2017). These methods explain the principle of protein-protein interactions from different aspects.…”

Section: Introductionmentioning

confidence: 99%

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network

et al. 2020

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Protein-protein interactions are the foundations of cellular life activities. At present, the already known protein-protein interactions only account for a small part of the total. With the development of experimental and computing technology, more and more PPI data are mined, PPI networks are more and more dense. It is possible to predict protein-protein interaction from the perspective of network structure. Although there are many high-throughput experimental methods to detect protein-protein interactions, the cost of experiments is high, time-consuming, and there is a certain error rate meanwhile. Network-based approaches can provide candidates of protein pairs for high-throughput experiments and improve the accuracy rate. This paper presents a new link prediction approach "Sim" for PPI networks from the perspectives of proteins' complementary interfaces and gene duplication. By integrating our approach "Sim" with the state-of-art network-based approach "L3," the prediction accuracy and robustness are improved.

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Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins

Cited by 17 publications

References 20 publications

Insights into the species-specific TLR4 signaling mechanism in response to Rhodobacter sphaeroides lipid A detection

Insights into the species-specific TLR4 signaling mechanism in response to Rhodobacter sphaeroides lipid A detection

Protein-Protein Interaction Detection: Methods and Analysis

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network

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