Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates
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Open access
Date
2024-04-30Type
- Journal Article
Abstract
Structure and function in nanoscale atomistic assemblies are tightly coupled, andevery atom with its specific position and even every electron will have a decisiveeffect on the electronic structure, and hence, on the molecular properties. Molec-ular simulations of nanoscopic atomistic structures therefore require accuratelyresolved three-dimensional input structures. If extracted from experiment, thesestructures often suffer from severe uncertainties, of which the lack of informationon hydrogen atoms is a prominent example. Hence, experimental structuresrequire careful review and curation, which is a time-consuming and error-proneprocess. Here, we present a fast and robust protocol for the automated structureanalysis and pH-consistent protonation, in short, ASAP. For biomolecules as atarget, the ASAP protocol integrates sequence analysis and error assessment of agiven input structure. ASAP allows for pKₐ prediction from reference data throughGaussian process regression including uncertainty estimation and connects tosystem-focused atomistic modeling described in Brunken and Reiher (J. Chem. TheoryComput.16, 2020, 1646). Although focused on biomolecules, ASAP can be extendedto other nanoscopic objects, because most of its design elements rely on a generalgraph-based foundation guaranteeing transferability. The modular character ofthe underlying pipeline supports different degrees of automation, which allows for(i) efficient feedback loops for human-machine interaction with a low entrance barrierand for (ii) integration into autonomous procedures such as automated force fieldparametrizations. This facilitates fast switching of the pH-state through on-the-flysystem-focused reparametrization during a molecular simulation at virtually no extracomputational cost. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000649816Publication status
publishedExternal links
Journal / series
Journal of Computational ChemistryVolume
Pages / Article No.
Publisher
WileySubject
atomistic simulation; Gaussian process; machine learning; protein structureOrganisational unit
03736 - Reiher, Markus / Reiher, Markus
Funding
182400 - Exhaustive First-Principles Exploration of Chemical Reaction Networks for Catalysis Design (SNF)
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