scholarly journals Protein Clustering in Formation of Falciparum Plasmodium using Soft Regularized-Markov Clustering Algorithm

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Hafizh Amrullah ◽  
Syamsuddin Wisnubroto
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Interaction ◽  
Execution Time ◽  
Clustering Algorithm ◽  
Interaction Network ◽  
Cluster Center ◽  
Clustering Method ◽  
Large Protein ◽  
Markov Clustering ◽  
Inflation Parameter

AbstractProtein has an important role in our life. Every protein interacts with other proteins, DNA, and other molecules. It forms a very large protein interaction networks. We need clustering method to analyze it. Soft Regularized Markov Clustering (SR-MCL) algorithm is one of clustering method to reduce the weakness of Regularized Markov Clustering and Markov Clustering.  In this research, SR-MCL will be applied using OpenMP.  In every thread, SR-MCL is run using inflation parameter r = 2, 3, and 4. The simulation results show that, based on the fastest execution time and the smallest iteration, the parameter r = 2 produces the best cluster with 40 iterations and execution time is 613 seconds. The cluster centers obtained are 49 clusters with the largest cluster center is the XPO1 protein that interacts with 662 proteins, and 17 protein pairs that interact with each other. Therefore, the XPO1 is a very influential protein in Plasmodium Falciparum.Keywords: SR-MCL Algorithm, Protein Interaction Network, Plasmodium Falciparum. AbstrakProtein memiliki peranan yang sangat penting dalam kehidupan. Setiap  protein berinteraksi  dengan  protein-protein  lain,  DNA,  dan  molekul-molekul  lainnya, sehingga  terbentuklah  jaringan  interaksi  protein  yang  berukuran  sangat  besar. Untuk memudahkan dalam menganalisisnya, diperlukan metode clustering. Algoritma  Soft  Regularized  Markov  Clustering  (SR-MCL)  yang  merupakan pengembangan metode clustering untuk mengurangi kelemahan dari Regularized Markov  Clustering  dan Markov  Clustering.  Pada  penelitian  ini,  SR-MCL  akan diterapkan  menggunakan  OpenMP,  yaitu  setiap  thread  menjalankan  SR-MCL dengan  parameter  inflasi  r  =  2,  3,  dan  4.  Hasil simulasi menunjukkan bahwa, berdasarkan waktu eksekusi tercepat dan iterasi terkecil, cluster terbaik diperoleh ketika r = 2 yang menghasilkan 40 iterasi dengan waktu eksekusi 613 detik. Pusat cluster adalah protein XPO1 yang berinteraksi dengan 662 protein dan 17 pasangan protein yang saling berinteraksi satu dengan lainnya. Oleh karena itu, protein XPO1 adalah protein yang sangat berpengaruh dalam pembentukan Plasmodium Falciparum.Kata kunci: Algoritma SR-MCL, Jaringan Interaksi Protein, Plasmodium Falciparum.

The Implementation of Regularized Markov Clustering with Pigeon Inspired Optimization Algorithm in Analyzing the SARS-CoV-2 (COVID-19) Protein Interaction Network

2020 ◽  
Vol 3 (3) ◽  
pp. 191-200
Author(s):  
M. Syamsuddin Wisnubroto ◽  
Marsudi Siburian ◽  
Febri Dwi Irawati
Keyword(s):  
Protein Interaction ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Drug Research ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Clustering Methods ◽  
Markov Clustering

Proteins interact with other proteins, DNA, and other molecules, forming large-scale protein interaction networks and for easy analysis, clustering methods are needed. Regularized Markov clustering algorithm is an improvement of MCL where operations on expansion are replaced by new operations that update the flow distributions of each node. But to reduce the weaknesses of the RMCL optimization, Pigeon Inspired Optimization Algorithm (PIO) is used to replace the inflation parameters. The simulation results of IPC SARS-Cov-2 (COVID-19) inflation parameters  get the result of 42 proteins as the center of the cluster and 8 protein pairs interacting with each other. Proteins of COVID-19 that interact with 20 or more proteins are ORF8, NSP13, NSP7, M, N, ORF9C, NSP8, and NSP1. Their interactions might be used as a target for drug research.

Dynamics of Protein-Protein Interaction Network in Plasmodium Falciparum

2009 ◽  
pp. 257-284
Author(s):  
Smita Mohanty ◽  
Shashi Bhushan Pandit ◽  
Narayanaswamy Srinivasan
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Cellular Localization ◽  
Interaction Network ◽  
Malarial Parasite ◽  
Strategic Integration ◽  
Protein Protein Interaction ◽  
Cellular Processes ◽  
Expression Of Genes

Integration of organism-wide protein interactome data with information on expression of genes, cellular localization of proteins and their functions has proved extremely useful in developing biologically intuitive interaction networks. This chapter highlights the dynamics in protein interaction network across different stages in the lifecycle of Plasmodium falciparum, a malarial parasite, and the implication of the network dynamics in different physiological processes. The main focus of the chapter is the integration of information on experimentally derived interactions of P.falciparum proteins with expression data and analysis of the implications of interactions in different cellular processes. Extensive analysis has been made to quantify the interaction dynamics across various stages, as well as correlating it with the dynamics of the cellular pathways involving the interacting proteins. The authors’ analysis demonstrates the power of strategic integration of genome-wide datasets in extracting information on dynamics of biological pathways and processes.

A protein interaction network of the malaria parasite Plasmodium falciparum

Nature ◽  
2005 ◽  
Vol 438 (7064) ◽  
pp. 103-107 ◽  
Author(s):  
Douglas J. LaCount ◽  
Marissa Vignali ◽  
Rakesh Chettier ◽  
Amit Phansalkar ◽  
Russell Bell ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Malaria Parasite ◽  
Interaction Network ◽  
Parasite Plasmodium ◽  
Parasite Plasmodium Falciparum

Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs

2012 ◽  
Vol 1 (2) ◽  
pp. 35-47
Author(s):  
Charalampos Moschopoulos ◽  
Grigorios Beligiannis ◽  
Spiridon Likothanassis ◽  
Sophia Kossida
Keyword(s):  
Genetic Algorithm ◽  
Protein Interaction ◽  
Clustering Algorithm ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Interaction Graphs ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Successful Prediction ◽  
Markov Clustering

In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.

Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs

2013 ◽  
pp. 805-816
Author(s):  
Charalampos Moschopoulos ◽  
Grigorios Beligiannis ◽  
Spiridon Likothanassis ◽  
Sophia Kossida
Keyword(s):  
Genetic Algorithm ◽  
Protein Interaction ◽  
Clustering Algorithm ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Interaction Graphs ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Markov Clustering ◽  
Complex Detection

In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.

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