scholarly journals PPI-GA: A Novel Clustering Algorithm to Identify Protein Complexes within Protein-Protein Interaction Networks Using Genetic Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Naeem Shirmohammady ◽  
Habib Izadkhah ◽  
Ayaz Isazadeh
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Protein Complexes ◽  
Protein Chemistry ◽  
Protein Protein Interaction ◽  
Interdisciplinary Field ◽  
Interactive Network ◽  
Real World Datasets ◽  
Encoding Method ◽  
Genetic Diversity Study

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks (PPI). To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms.

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.

scholarly journals Identifying Protein Complexes in Protein-protein Interaction Data using Graph Convolution Network

2021 ◽  
Author(s):  
Nazar Zaki ◽  
Harsh Singh
Keyword(s):  
Protein Interaction ◽  
Clustering Algorithm ◽  
Sparse Matrix ◽  
Protein Complexes ◽  
Protein Detection ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Dense Matrix ◽  
Protein Protein Interaction ◽  
Matrix Operations

Protein complexes are groups of two or more polypeptide chains that join together to build noncovalent networks of protein interactions. A number of means of computing the ways in which protein complexes and their members can be identified from these interaction networks have been created. While most of the existing methods identify protein complexes from the protein-protein interaction networks (PPIs) at a fairly decent level, the applicability of advanced graph network methods has not yet been adequately investigated. In this paper, we proposed various graph convolutional networks (GCNs) methods to improve the detection of the protein functional complexes. We first formulated the protein complex detection problem as a node classification problem. Second, the Neural Overlapping Community Detection (NOCD) model was applied to cluster the nodes (proteins) using a complex affiliation matrix. A representation learning approach, which combines the multi-class GCN feature extractor (to obtain the features of the nodes) and the mean shift clustering algorithm (to perform clustering), is also presented. We have also improved the efficiency of the multi-class GCN network to reduce space and time complexities by converting the dense-dense matrix operations into dense-spares or sparse-sparse matrix operations. This proposed solution significantly improves the scalability of the existing GCN network. Finally, we apply clustering aggregation to find the best protein complexes. A grid search was performed on various detected complexes obtained by applying three well-known protein detection methods namely ClusterONE, CMC, and PEWCC with the help of the Meta-Clustering Algorithm (MCLA) and Hybrid Bipartite Graph Formulation (HBGF) algorithm. The proposed GCN-based methods were tested on various publicly available datasets and provided significantly better performance than the previous state-of-the-art methods. The code and data used in this study are available from https://github.com/Analystharsh/GCN_complex_detection.

scholarly journals Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Flow Shop ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Multimodal Optimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

scholarly journals Phage-Display Based Discovery and Characterization of Peptide Ligands against WDR5

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1225
Author(s):  
Jiawen Cao ◽  
Tiantian Fan ◽  
Yanlian Li ◽  
Zhiyan Du ◽  
Lin Chen ◽  
...  
Keyword(s):  
Phage Display ◽  
Cyclic Peptide ◽  
Protein Complexes ◽  
Peptide Ligands ◽  
Phage Library ◽  
Cancer Drug ◽  
Protein Protein Interaction ◽  
Phage Display Technique ◽  
Peptide Mimic ◽  
Human Proteins

WD40 is a ubiquitous domain presented in at least 361 human proteins and acts as scaffold to form protein complexes. Among them, WDR5 protein is an important mediator in several protein complexes to exert its functions in histone modification and chromatin remodeling. Therefore, it was considered as a promising epigenetic target involving in anti-cancer drug development. In view of the protein–protein interaction nature of WDR5, we initialized a campaign to discover new peptide-mimic inhibitors of WDR5. In current study, we utilized the phage display technique and screened with a disulfide-based cyclic peptide phage library. Five rounds of biopanning were performed and isolated clones were sequenced. By analyzing the sequences, total five peptides were synthesized for binding assay. The four peptides are shown to have the moderate binding affinity. Finally, the detailed binding interactions were revealed by solving a WDR5-peptide cocrystal structure.

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