GReMLIN: A Graph Mining Strategy to Infer Protein-Ligand Interaction Patterns

Author(s):  
Charles A. Santana ◽  
Fabio R. Cerqueira ◽  
Carlos H. Da Silveira ◽  
Alexandre V. Fassio ◽  
Raquel C. De Melo-Minardi ◽  
...  
2018 ◽  
Author(s):  
Aida Mrzic ◽  
Dries Van Rompaey ◽  
Stefan Naulaerts ◽  
Hans De Winter ◽  
Wim Vanden Berghe ◽  
...  

In recent years, the pharmaceutical industry has been confronted with rising R&D costs paired with decreasing productivity. Attrition rates for new molecules are tremendous, with a substantial number of molecules failing in an advanced stage of development. Repositioning previously approved drugs for new indications can mitigate these issues by reducing both risk and cost of development. Computational methods have been developed to allow for the prediction of drug-target interactions, but it remains difficult to branch out into new areas of application where information is scarce. Here, we present a proof-of-concept for discovering patterns in protein-ligand data using frequent itemset mining. Two key advantages of our method are the transferability of our patterns to different application domains and the facile interpretation of our recommendations. Starting from a set of known protein-ligand relationships, we identify patterns of molecular substructures and protein domains that lie at the basis of these interactions. We show that these same patterns also underpin metabolic pathways in humans. We further demonstrate how association rules mined from human protein-ligand interaction patterns can be used to predict antibiotics susceptible to bacterial resistance mechanisms.


2018 ◽  
Author(s):  
Aida Mrzic ◽  
Dries Van Rompaey ◽  
Stefan Naulaerts ◽  
Hans De Winter ◽  
Wim Vanden Berghe ◽  
...  

In recent years, the pharmaceutical industry has been confronted with rising R&D costs paired with decreasing productivity. Attrition rates for new molecules are tremendous, with a substantial number of molecules failing in an advanced stage of development. Repositioning previously approved drugs for new indications can mitigate these issues by reducing both risk and cost of development. Computational methods have been developed to allow for the prediction of drug-target interactions, but it remains difficult to branch out into new areas of application where information is scarce. Here, we present a proof-of-concept for discovering patterns in protein-ligand data using frequent itemset mining. Two key advantages of our method are the transferability of our patterns to different application domains and the facile interpretation of our recommendations. Starting from a set of known protein-ligand relationships, we identify patterns of molecular substructures and protein domains that lie at the basis of these interactions. We show that these same patterns also underpin metabolic pathways in humans. We further demonstrate how association rules mined from human protein-ligand interaction patterns can be used to predict antibiotics susceptible to bacterial resistance mechanisms.


2013 ◽  
Vol 53 (3) ◽  
pp. 623-637 ◽  
Author(s):  
Jérémy Desaphy ◽  
Eric Raimbaud ◽  
Pierre Ducrot ◽  
Didier Rognan

Author(s):  
Xiaodong Pang ◽  
Linxiang Zhou ◽  
Lily Zhang ◽  
Lina Xu ◽  
Xinyi Zhang

Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...


RSC Advances ◽  
2019 ◽  
Vol 9 (14) ◽  
pp. 7757-7766 ◽  
Author(s):  
Yao Wu ◽  
Xin-Ying Gao ◽  
Xin-Hui Chen ◽  
Shao-Long Zhang ◽  
Wen-Juan Wang ◽  
...  

Our study gains insight into the development of novel specific ABCG2 inhibitors, and develops a comprehensive computational strategy to understand protein ligand interaction with the help of AlphaSpace, a fragment-centric topographic mapping tool.


2021 ◽  
Vol 17 ◽  
Author(s):  
Avram Speranta ◽  
Laura Manoliu ◽  
Catalina Sogor ◽  
Maria Mernea ◽  
Corina Duda Seiman ◽  
...  

Background: During the current SARS-CoV-2 pandemic, the identification of effective antiviral drugs is crucial. Unfortunately, no specific treatment or vaccine is available to date. Objective: Here, we aimed to predict the interactions between SARS-CoV-2 proteins and protein targets from the human body for some flavone molecules (kaempferol, morin, pectolinarin, myricitrin, and herbacetin) in comparison to synthetic compounds (hydroxychloroquine, remdesivir, ribavirin, ritonavir, AMD-070, favipiravir). Methods: Using MOE software and advanced bioinformatics and cheminformatics portals, we conducted an extensive analysis based on various structural and functional features of compounds, such as their amphiphilic field, flexibility, and steric features. The structural similarity analysis of natural and synthetic compounds was performed using Tanimoto coefficients. The interactions of some compounds with SARS-CoV-2 3CLprotease or RNA-dependent RNA polymerase were described using 2D protein-ligand interaction diagrams based on known crystal structures. The potential targets of considered compounds were identified using the SwissTargetPrediction web tool. Results: Our results showed that remdesivir, pectolinarin, and ritonavir present a strong structural similarity which may be correlated to their similar biological activity. As common molecular targets of compounds in the human body, ritonavir, kaempferol, morin, and herbacetin can activate multidrug resistance-associated proteins, while remdesivir, ribavirin, and pectolinarin appear as ligands for adenosine receptors. Conclusion: Our evaluation recommends remdesivir, pectolinarin, and ritonavir as promising anti-SARS-CoV-2 agents.


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