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The role of water in host-guest interaction

Valerio Rizzi1,2*, Luigi Bonati3,2*, Narjes Ansari1,2*, Michele Parrinello1,2,4*

1 Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland

2 Facoltà di Informatica, Istituto di Scienze Computazionali, Università della Svizzera Italiana, Via G. Buffi 13, 6900 Lugano, Switzerland

3 Department of Physics, ETH Zurich, 8092 Zurich, Switzerland

4 Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy

* Corresponding authors emails: valerio.rizzi@phys.chem.ethz.ch, luigi.bonati@phys.chem.ethz.ch, nansari@ethz.ch, parrinello@phys.chem.ethz.ch
DOI10.24435/materialscloud:p3-1x [version v1]

Publication date: Sep 28, 2020

How to cite this record

Valerio Rizzi, Luigi Bonati, Narjes Ansari, Michele Parrinello, The role of water in host-guest interaction, Materials Cloud Archive 2020.112 (2020), doi: 10.24435/materialscloud:p3-1x.


One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

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File name Size Description
3.3 GiB Compressed input files for all the investigated systems in the paper. For each case, one trajectory with the corresponding collective variables output is provided.
1.5 KiB README file


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Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.


ligand binding water host-guest SAMPL5 enhanced sampling Neural Network molecular dynamics SNSF MARVEL/DD1 ERC

Version history:

2020.112 (version v1) [This version] Sep 28, 2020 DOI10.24435/materialscloud:p3-1x