ArticleNature communications2015
The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 112 citations in OpenAlex.
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- First principles molecular dynamics simulation and thermal decomposition kinetics study of CL-20.Journal of molecular modeling · 2024Article
- Molecular Dynamics and Machine Learning Give Insights on the Flexibility-Activity Relationships in Tyrosine Kinome.Journal of chemical information and modeling · 2023Article
- Long-time-step molecular dynamics can retard simulation of protein-ligand recognition process.Biophysical journal · 2023Article
- Anticancer Activity, Reduction Mechanism and G-Quadruplex DNA Binding of a Redox-Activated Platinum(IV)-Salphen Complex.International journal of molecular sciences · 2022Article
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- Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.Current opinion in chemical biology · 2022Review
- Binding Networks Identify Targetable Protein Pockets for Mechanism-Based Drug Design.International journal of molecular sciences · 2022Article
- Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases.Marine drugs · 2022Article
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- New perspectives in cancer drug development: computational advances with an eye to design.RSC medicinal chemistry · 2021Review
- Binding thermodynamics and interaction patterns of human purine nucleoside phosphorylase-inhibitor complexes from extensive free energy calculations.Journal of computer-aided molecular design · 2021Article
- EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery.Protein science : a publication of the Protein Society · 2021Article
- Tuning Local Hydration Enables a Deeper Understanding of Protein-Ligand Binding: The PP1-Src Kinase Case.The journal of physical chemistry letters · 2021Article
- Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations.Frontiers in molecular biosciences · 2021Article
- Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation.Chemical reviews · 2020Review
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- Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.Frontiers in molecular biosciences · 2020Review
- Predicting and designing therapeutics against the Nipah virus.PLoS neglected tropical diseases · 2019Article
- Molecular insights into the interaction of hemorphin and its targets.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe-immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as kon and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe-immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug-target recognition and binding.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.