Controlling Directed Protein Interaction Networks in Cancer

Kanhaiya, Krishna; Czeizler, Eugen; Gratie, Cristian; Petre, Ion

doi:10.1038/s41598-017-10491-y

Cited by 69 publications

(83 citation statements)

References 77 publications

(80 reference statements)

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“…They postulated that network controllability analysis would be able to aid in validating the druggable genome, as well as identify novel therapeutic targets [ 179 ]. Control theory and network analysis have been applied in other similar studies to identify essential nodes for cancer [ 180 ] and viral infection [ 181 ].…”

Section: Emerging In Silico Approaches For Ppi Drug Discoverymentioning

confidence: 99%

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

et al. 2018

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The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

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Section: Emerging In Silico Approaches For Ppi Drug Discoverymentioning

confidence: 99%

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

et al. 2018

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“…Анализ контролируемости сетей белок белковых взаимодействий применительно к раковым клеткам представлен в работе [57]. В ней было показано, что для управления полной интерактомной сетью требуется контроль над приблизительно 70% всех белков, в то время как для управления сетью КВОГ требуется контролировать только 6% всех узлов сети.…”

Section: интерактомикаunclassified

Searching for essential genes in cancer genomes

2018

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The concept of essential genes, whose loss of functionality leads to cell death, is one of the fundamental concepts of genetics and is important for fundamental and applied research. This field is particularly promising in relation to oncology, since the search for genetic vulnerabilities of cancer cells allows us to identify new potential targets for antitumor therapy. The modern biotechnology capacities allow carrying out large-scale projects for sequencing somatic mutations in tumors, as well as directly interfering the genetic apparatus of cancer cells. They provided accumulation of a considerable body of knowledge about genetic variants and corresponding phenotypic manifestations in tumors. In the near future this knowledge will find application in clinical practice. This review describes the main experimental and computational approaches to the search for essential genes, concentrating on the application of these methods in the field of molecular oncology.

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“…The directed PPI cancer data, uses SIGNOR (SIGnaling Network Open Resource) database [ 33 ], which outputs binary matrix representations for the used-provided protein lists and allows us to create directed graphs between signaling entities. The networks are available in Network Controlability Project [ 34 ] or seen (Additional file 3 : Table S2). In our paper, in total 1507 approved proteins (or genes) by the Food and Drug Administration (FDA) have been selected as the constrained (or preselected) nodes in the directed PPI network which will have a prior-known molecule mechanism, see (Additional file 4 : Table S3).…”

Section: Resultsmentioning

confidence: 99%

“…In the Ref. [ 34 ], researchers collected essential gene data for all cancer from the COLT-Cancer database [ 37 ]. In particular, they considered the HPAF-II cell lines for pancreatic cancer, and follow the GARP (Gene Activity Rank Profile) and GARP- P value of corresponding proteins mentioned in the database.…”

Section: Resultsmentioning

confidence: 99%

A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

et al. 2018

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BackgroundThe advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes).ResultsTherefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng.ConclusionsIn the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12864-017-4332-z) contains supplementary material, which is available to authorized users.

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Controlling Directed Protein Interaction Networks in Cancer

Cited by 69 publications

References 77 publications

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

Searching for essential genes in cancer genomes

A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

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