scholarly journals Using informative features in machine learning based method for COVID-19 drug repurposing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Rosa Aghdam ◽  
Mahnaz Habibi ◽  
Golnaz Taheri
Keyword(s):  
Drug Target ◽  
Clinical Evidence ◽  
Biological Network ◽  
Drug Repurposing ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Major Disruption ◽  
Novel Virus ◽  
Clinical Profiles ◽  
The Cost

AbstractCoronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug–target and protein−protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

scholarly journals Critically ill COVID-19 status associated trait genetics reveals CDK6 inhibitors as potential treatment

2021 ◽  
Author(s):  
Joern Klinger ◽  
Charles Ravarani ◽  
Colin Bannard ◽  
Margaretha Lamparter ◽  
Alexander Schwinges ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Critically Ill ◽  
Body Mass ◽  
Drug Target ◽  
Clinical Evidence ◽  
Drug Repurposing ◽  
Cell Types ◽  
Selective Treatment ◽  
Reactive Protein ◽  
Prior Literature

Abstract Despite the recent development of vaccines and monoclonal antibodies preventing SARS-CoV-2 infection, treating critically ill COVID-19 patients still remains a top goal. In principle, drug repurposing – the use of an already existing drug for a new indication – could provide a shortcut to a treatment. However, drug repurposing is often very speculative due to lack of clinical evidence. We report here on a methodology to find and test drug target candidates for drug repurposing. Using UK Biobank data, we matched critically ill COVID-19 cases with healthy controls and screened for significant differences in 33 blood cell types, 30 blood biochemistries, and body mass index. Significant differences in traits that have been associated with critically ill COVID-19 status in prior literature, such as alanine aminotransferase, body mass index, C-reactive protein, and neutrophil cell count, were further investigated. In-depth statistical analysis of COVID-19 associated traits and their genetics using regression modeling and propensity score stratification identified cyclin-dependent kinase 6 (CDK6) as a more promising drug target for the selective treatment of critically ill COVID-19 patients than the previously reported interleukin 6. Four existing CDK6 inhibitors -- abemaciclib, ribociclib, trilaciclib, and palbociclib -- have been approved for the treatment of breast cancer. Clinical evidence for CDK6 inhibitors in treating critically ill COVID-19 patients has been reported. Further clinical investigations are ongoing.

scholarly journals A mixed clustering coefficient centrality for identifying essential proteins

2020 ◽  
Vol 34 (10) ◽  
pp. 2050090
Author(s):  
Pengli Lu ◽  
JingJuan Yu
Keyword(s):  
Drug Target ◽  
Protein Complexes ◽  
Biological Evolution ◽  
Clustering Coefficient ◽  
Biological Information ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Cell Life ◽  
Definition Of

Essential protein plays a crucial role in the process of cell life. The identification of essential proteins not only promotes the development of drug target technology, but also contributes to the mechanism of biological evolution. There are plenty of scholars who pay attention to discover essential proteins according to the topological structure of protein network and biological information. The accuracy of protein recognition still demands to be improved. In this paper, we propose a method which integrates the clustering coefficient in protein complexes and topological properties to determine the essentiality of proteins. First, we give the definition of In-clustering coefficient (IC) to describe the properties of protein complexes. Then we propose a new method, complex edge and node clustering (CENC) coefficient, to identify essential proteins. Different Protein–Protein Interaction (PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as experimental materials. Through some experiments of logistic regression model, the results show that the method of CENC can promote the ability of recognizing essential proteins by comparing with the existing methods DC, BC, EC, SC, LAC, NC and the recent UC method.

scholarly journals Critically ill COVID-19 associated trait genetics reveals CDK6 inhibitors as potential treatment

2021 ◽  
Author(s):  
Joern E. Klinger ◽  
Charles N. J. Ravarani ◽  
Colin Bannard ◽  
Margaretha R. J. Lamparter ◽  
Alexander R. E. C. Schwinges ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Critically Ill ◽  
Body Mass ◽  
Drug Target ◽  
Clinical Evidence ◽  
Drug Repurposing ◽  
Cell Types ◽  
Reactive Protein ◽  
The Uk ◽  
Propensity Score Stratification

Despite the recent development of vaccines and monoclonal antibodies preventing SARS-CoV-2 infection, treating critically ill COVID-19 patients still remains a top goal. In principle, drug repurposing, the use of an already existing drug for a new indication, could provide a shortcut to a treatment. However, drug repurposing is often very speculative due to the lack of clinical evidence. We here report on a methodology to find and test gene drug target candidates for drug repurposing. We matched critically ill COVID-19 cases from the UK Biobank with healthy controls and screened for significant differences in 33 blood cell types, 30 blood biochemistries, and body mass index in cases and controls. Significant differences in traits that have previously been associated with critically ill COVID-19 status, such as alanine aminotransferase, body mass index, C-reactive protein, and neutrophil cell count were further investigated. In-depth statistical analysis of COVID-19 associated traits and their genetics using regression modeling and propensity score stratification identified cyclin-dependent kinase 6 (CDK6) as a more promising drug target to selectively treat critically ill COVID-19 patients than the previously reported interleukin 6. Four existing CDK6 inhibitors abemaciclib, ribociclib, trilaciclib, and palbociclib have been approved for breast cancer. Clinical evidence for CDK6 inhibitors in treating critically ill COVID-19 has been reported. Further clinical investigations are ongoing.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

Identification of Essential Proteins in Yeast Using Mean Weighted Average and Recursive Feature Elimination

2019 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Sivagnanam Rajamanickam Mani Sekhar ◽  
Siddesh Gaddadevara Matt ◽  
Sunilkumar S. Manvi ◽  
Srinivasa Krishnarajanagar Gopalalyengar
Keyword(s):  
Drug Design ◽  
Weighted Average ◽  
Living Organism ◽  
Experimental Result ◽  
Recursive Feature Elimination ◽  
Protein Interaction Data ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Result Show ◽  
Better Than

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

scholarly journals An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3174 ◽  
Author(s):  
Xin Xue ◽  
Gang Bao ◽  
Hai-Qing Zhang ◽  
Ning-Yi Zhao ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Target ◽  
P53 Protein ◽  
Pharmacophore Model ◽  
Cellular Level ◽  
Complex Structures ◽  
Ligand Complex ◽  
Null Cell ◽  
Protein Protein Interaction ◽  
First Time

: The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

scholarly journals A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

2021 ◽  
Author(s):  
Zhihong Zhang ◽  
Sai Hu ◽  
Wei Yan ◽  
Bihai Zhao ◽  
Lei Wang
Keyword(s):  
Protein Interaction ◽  
Matrix Factorization ◽  
Biological Data ◽  
Protein Domain ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Non Negative Matrix Factorization

Abstract BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

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