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Structure and function in nanoscale atomistic assemblies are tightly coupled,and every atom with its specific position and even every electron will have adecisive effect on the electronic structure, and hence, on the molecularproperties. Molecular simulations of nanoscopic atomistic structures thereforerequire accurately resolved three-dimensional input structures. If extractedfrom experiment, these structures often suffer from severe uncertainties, ofwhich the lack of information on hydrogen atoms is a prominent example. Hence,experimental structures require careful review and curation, which is atime-consuming and error-prone process. Here, we present a fast and robustprotocol for the automated structure analysis, and pH-consistent protonation,in short, ASAP. For biomolecules as a target, the ASAP protocol integratessequence analysis and error assessment of a given input structure. ASAP allowsfor pKa prediction from reference data through Gaussian process regressionincluding uncertainty estimation and connects to system-focused atomisticmodeling described in (J. Chem. Theory Comput. 16, 2020, 1646). Althoughfocused on biomolecules, ASAP can be extended to other nanoscopic objects,because most of its design elements rely on a general graph-based foundationguaranteeing transferability. The modular character of the underlying pipelinesupports different degrees of automation, which allows for (i) efficientfeedback loops for human-machine interaction with a low entrance barrier andfor (ii) integration into autonomous procedures such as automated force fieldparametrizations. This facilitates fast switching of the pH-state throughon-the-fly system-focused reparametrization during a molecular simulation atvirtually no extra computational cost.

Automated Preparation of Nanoscopic Structures: Graph-Based Sequence  Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty  Estimates