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By Gerard Martínez-Rosell

PlayMolecule Introduction

One step of the simulation workflow has typically remained relatively underdressed, namely, the preparation steps before building a molecular system. These preparation steps aim to make a protein structure, usually extracted from the PDB database, ready for system building. In particular, there’s two main points that need to be addressed: (a) titration of the residues and (b) optimization of hydrogen bond (H-bond) network. Resolved protein structures usually don’t contain hydrogen atoms and therefore need to be added by the researcher before running a molecular dynamics (MD) simulation. While most hydrogens can be easily guessed, some protein residues prove to be more challenging as they co-exist in different protonation states. While in a constant-pH simulation the residues would be free to switch among the different protonation states, in a classic MD simulation protonation states are fixed and therefore must be decided beforehand. These protonation states greatly depend on the local environment of the residue and the simulation pH.

PlayMolecule ProteinPrepare

For this reason, we devised ProteinPrepare, a protein preparation web application that allows the estimation and visualization of charge states, optimization of the hydrogen bonding network of protonated structures, thorough visual inspection of the results, and rapid iteration of changes. The application provides an evaluation of the titration states of a target protein’s residues on the basis of their local environment and the optimization of its hydrogen-bonding network through the placement of missing hydrogen atoms and flipping of side chains. Special consideration is given to residues whose pKa is close to the solvent pH because they are more prone to be misclassified by the customary binary (protonated vs unprotonated) assignments; the user can interact with the results, force their chosen titration states, and have the application reoptimize the structure. The computation is executed on the server taking advantage of High-Throughput Molecular Dynamics (HTMD) a Python framework for simple molecular-simulation-based discovery. In particular, we used the proteinPrepare() functionality of HTMD, currently based on PROPKA 3.1 and PDB2PQR 2.1. As such, in contrast to most other graphical tools, this web application also provides the short HTMD Python code required to perform the same task offline for the specific structure. The web application is publicly available at www. playmolecule.org for use on the web as part of the PlayMolecule web platform.

Conclusion

The protein preparation tool presented offers three main features: (1) help in deciding the protonation states and charges of residues while still retaining the capability to change them, (2) optimization of the hydrogen-bonding network as it might have been approximative from the crystal structure, and (3) both an interactive and scriptable way to perform these tasks. Deciding on protonation states in classical molecular dynamics is critical because a change in charge can have drastic effects and invalidate all of the results. The hydrogen-bonding network optimization is important to help the protein retain the original state of the crystal structure, especially when losing an active or inactive state can take milliseconds or more to recover. The third aspect is purely one of convenience: the use of the HTMD framework allows the reproduction of any result obtained through the ProteinPrepare web interface on local computing resources and the possibility to automate the preparation steps, e.g., repeating them for a large set of structures in a high-throughput context. Coding of HTMD based analysis on local resources takes place through the Python language and can thus take advantage of any of its numerous libraries and facilities for reproducible research.

References

Martínez-Rosell, G.; Giorgino, T.; De Fabritiis, G.; J. Chem. Inf. Model., 2017, 57 (7), pp 1511–1516
Doerr, S.; Harvey, M. J.; Noe, F.; De Fabritiis, G.; J. Chem. Theory Comput. 2016 , 12, 1845 −1852.
Søndergaard, C. R.; Olsson, M. H. M.; Rostkowski, M.; Jensen, J. H. J. Chem. Theory Comput. 2011 , 7, 2284 −2295.
Dolinsky, T. J.; Czodrowski, P.; Li, H.; Nielsen, J. E.; Jensen, J. H.; Klebe, G.; Baker, N. A. Nucleic Acids Res. 2007 , 35, W522 −W525.

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