Encoding Protein–Ligand Interaction Patterns in Fingerprints and Graphs

2013 ◽  
Vol 53 (3) ◽  
pp. 623-637 ◽  
Author(s):  
Jérémy Desaphy ◽  
Eric Raimbaud ◽  
Pierre Ducrot ◽  
Didier Rognan
Keyword(s):  
Interaction Patterns ◽  
Ligand Interaction ◽  
Protein Ligand Interaction ◽  
Encoding Protein

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

2016 ◽  
Author(s):  
Charles A. Santana ◽  
Fabio R. Cerqueira ◽  
Carlos H. Da Silveira ◽  
Alexandre V. Fassio ◽  
Raquel C. De Melo-Minardi ◽  
...  
Keyword(s):  
Graph Mining ◽  
Interaction Patterns ◽  
Ligand Interaction ◽  
Protein Ligand Interaction

scholarly journals Automatic discovery of transferable patterns in protein-ligand interaction networks

2018 ◽  
Author(s):  
Aida Mrzic ◽  
Dries Van Rompaey ◽  
Stefan Naulaerts ◽  
Hans De Winter ◽  
Wim Vanden Berghe ◽  
...  
Keyword(s):  
Drug Target ◽  
Bacterial Resistance ◽  
Resistance Mechanisms ◽  
Frequent Itemset ◽  
Interaction Patterns ◽  
Ligand Interaction ◽  
Attrition Rates ◽  
Stage Of Development ◽  
Protein Ligand Interaction ◽  
Approved Drugs

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.

scholarly journals Automatic discovery of transferable patterns in protein-ligand interaction networks

2018 ◽  
Author(s):  
Aida Mrzic ◽  
Dries Van Rompaey ◽  
Stefan Naulaerts ◽  
Hans De Winter ◽  
Wim Vanden Berghe ◽  
...  
Keyword(s):  
Drug Target ◽  
Bacterial Resistance ◽  
Resistance Mechanisms ◽  
Frequent Itemset ◽  
Interaction Patterns ◽  
Ligand Interaction ◽  
Attrition Rates ◽  
Stage Of Development ◽  
Protein Ligand Interaction ◽  
Approved Drugs

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.

scholarly journals Two Rules on the Protein-Ligand Interaction

2008 ◽  
Author(s):  
Xiaodong Pang ◽  
Linxiang Zhou ◽  
Lily Zhang ◽  
Lina Xu ◽  
Xinyi Zhang
Keyword(s):  
Ligand Interaction ◽  
Protein Ligand Interaction

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

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...

scholarly journals Fragment-centric topographic mapping method guides the understanding of ABCG2-inhibitor interactions

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 ◽  
...  
Keyword(s):  
Mapping Method ◽  
Topographic Mapping ◽  
Ligand Interaction ◽  
Mapping Tool ◽  
Protein Ligand Interaction ◽  
Insight Into ◽  
Computational Strategy

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.

Structural bioinformatics used to predict the protein targets of remdesivir and flavones in SARS-CoV-2 infection

2021 ◽  
Vol 17 ◽  
Author(s):  
Avram Speranta ◽  
Laura Manoliu ◽  
Catalina Sogor ◽  
Maria Mernea ◽  
Corina Duda Seiman ◽  
...  
Keyword(s):  
Human Body ◽  
Specific Treatment ◽  
Structural Similarity ◽  
Web Tool ◽  
Ligand Interaction ◽  
Protein Targets ◽  
Synthetic Compounds ◽  
Associated Proteins ◽  
Functional Features ◽  
Protein Ligand Interaction

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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