scholarly journals Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information

2021 ◽  
pp. 115-125
Author(s):  
Jie Pan ◽  
Changqing Yu ◽  
Liping Li ◽  
Zhuhong You ◽  
Zhonghao Ren ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Computational Prediction ◽  
Sequence Information ◽  
Protein Protein Interactions

scholarly journals Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information

2021 ◽  
Author(s):  
Jie. Pan ◽  
Zhu Hong. You ◽  
Li Ping. Li ◽  
Chang-Qing. Yu ◽  
Xin-Ke. Zhan
Keyword(s):  
Plant Cell ◽  
Protein Interactions ◽  
Cell Function ◽  
Functional Organization ◽  
Computational Prediction ◽  
Support Vector ◽  
Svm Classifier ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Representation Scheme

Abstract Protein-protein interactions (PPIs) in plants plays a significant role in plant biology and functional organization of cells. Although, a large amount of plant PPIs data have been generated by high-throughput techniques, but due to the complexity of plant cell, the PPIs pairs currently obtained by experimental methods cover only a small fraction of the complete plant PPIs network. In addition, the experimental approaches for identifying PPIs in plants are laborious, time-consuming, and costly. Hence, it is highly desirable to develop more efficient approaches to detect PPIs in plants. In this study, we present a novel computational model combining weighted sparse representation-based classifier (WSRC) with a novel inverse fast Fourier transform (IFFT) representation scheme which was adopted in position specific scoring matrix (PSSM) to extract features from plant protein sequence. When performed the proposed method on the plants PPIs dataset of Mazie, Rice and Arabidopsis thaliana (Arabidopsis), we achieved excellent results with high accuracies of 89.12%, 84.72% and 71.74%, respectively. To further assess the prediction performance of the proposed approach, we compared it with the state-of-art support vector machine (SVM) classifier. To the best of our knowledge, we are the first to employ protein sequences information to predict PPIs in plants. Experimental results demonstrate that the proposed method has a great potential to become a powerful tool for exploring the plant cell function.

scholarly journals Multimodal deep representation learning for protein interaction identification and protein family classification

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Da Zhang ◽  
Mansur Kabuka
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Representation Learning ◽  
Superior Performance ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Learning Framework ◽  
Topological Features ◽  
Ppi Networks ◽  
Ppi Prediction

Abstract Background Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. Results In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. Conclusion To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

scholarly journals UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Theodosios Theodosiou ◽  
Nikolaos Papanikolaou ◽  
Maria Savvaki ◽  
Giulia Bonetto ◽  
Stella Maxouri ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Computational Prediction ◽  
Biomedical Literature ◽  
Protein Network ◽  
Protein Protein Interactions ◽  
High Coverage ◽  
Depth Study ◽  
Experimental Approaches ◽  
Wet Lab ◽  
User Friendly

Abstract The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

scholarly journals Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Li ◽  
Zheng Wang ◽  
Li-Ping Li ◽  
Zhu-Hong You ◽  
Wen-Zhun Huang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Large Scale ◽  
False Positive Rate ◽  
Computational Method ◽  
Evolutionary Information ◽  
Local Alignment ◽  
Protein Interaction Data ◽  
Sequence Information ◽  
Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

scholarly journals Computational Prediction of Protein-Protein Interactions in Leishmania Predicted Proteomes

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e51304 ◽  
Author(s):  
Antonio M. Rezende ◽  
Edson L. Folador ◽  
Daniela de M. Resende ◽  
Jeronimo C. Ruiz
Keyword(s):  
Protein Interactions ◽  
Computational Prediction ◽  
Protein Protein Interactions

scholarly journals Computational Prediction of Protein-Protein Interactions of Human Tyrosinase

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Su-Fang Wang ◽  
Sangho Oh ◽  
Yue-Xiu Si ◽  
Zhi-Jiang Wang ◽  
Hong-Yan Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Rna Binding ◽  
3D Structure ◽  
Computational Prediction ◽  
Binding Motif ◽  
Protein Protein Interactions ◽  
Lim Domains ◽  
Binding Partners ◽  
Computational Predictions ◽  
Human Tyrosinase

The various studies on tyrosinase have recently gained the attention of researchers due to their potential application values and the biological functions. In this study, we predicted the 3D structure of human tyrosinase and simulated the protein-protein interactions between tyrosinase and three binding partners, four and half LIM domains 2 (FHL2), cytochrome b-245 alpha polypeptide (CYBA), and RNA-binding motif protein 9 (RBM9). Our interaction simulations showed significant binding energy scores of −595.3 kcal/mol for FHL2, −859.1 kcal/mol for CYBA, and −821.3 kcal/mol for RBM9. We also investigated the residues of each protein facing toward the predicted site of interaction with tyrosinase. Our computational predictions will be useful for elucidating the protein-protein interactions of tyrosinase and studying its binding mechanisms.

scholarly journals Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

2016 ◽  
Vol 113 (52) ◽  
pp. 15018-15023 ◽  
Author(s):  
Juan Rodriguez-Rivas ◽  
Simone Marsili ◽  
David Juan ◽  
Alfonso Valencia
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Accurate Information ◽  
Twilight Zone ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Protein Interfaces ◽  
Recent Developments

Protein–protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein–protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein–protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein–protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.

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