CherryPicker: An Algorithm for the Automated Parametrization of Large Biomolecules for Molecular Simulation

Welsh, Ivan D.; Allison, Jane R.

doi:10.3389/fchem.2019.00400

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…[74,110,111,113,128,131,[177][178][179][180][181][182][183][184][185][186][187][188]). We choose to discuss only a few in some more detail for illustrative purposes: Welsh et al 189 introduced the CHERRYPICKER algorithm, which was designed for large biomolecules by combination of a fragmentation approach and a graph-based database search of already parametrized subentities. Furthermore, the JOYCE and PICKY procedures for parametrization of intra-and intermolecular terms 178,190 are promising candidates for automated FF construction due to their high degree of flexibility with regard to the functional form and parameter origin.…”

Section: Automated Force Field Constructionmentioning

confidence: 99%

UniversalQM/MMapproaches for general nanoscale applications

Csizi

Reiher

2023

WIREs Comput Mol Sci

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Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black‐box‐type manner. Although diverse best‐practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom‐economical physically‐motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build de novo models based on first‐principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations. This article is categorized under: Electronic Structure Theory > Combined QM/MM Methods

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Section: Automated Force Field Constructionmentioning

confidence: 99%

UniversalQM/MMapproaches for general nanoscale applications

Csizi

Reiher

2023

WIREs Comput Mol Sci

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“…This workflow makes FF parametrization well suited for automated procedures [106] and multiple protocols have emerged that derive MM parameters from QM reference data in an iterative manner (see, for instance, [66,91,93,100,103,[146][147][148][149][150][151][152][153][154][155][156][157][158]). We choose to discuss only a few in some more detail for illustrative purposes: Welsh et al [159] introduced the CherryPicker algorithm which was designed for large biomolecules by combination of a fragmentation approach and a graph-based database search of already parametrized subentities. Furthermore, the Joyce and Picky procedures for parametrization of intra-and intermolecular terms [148,160] are promising candidates for automated FF construction due to their high degree of flexibility with regard to the functional form and parameter origin.…”

Section: Automated Force Field Constructionmentioning

confidence: 99%

Universal QM/MM Approaches for General Nanoscale Applications

Csizi¹,

Reiher²

2022

Preprint

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Hybrid quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. However, they are tedious to construct and they usually require significant manual preprocessing and expertise. As a result, these models may not be easily transferable to new application areas and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it has been difficult to devise automated procedures of controllable accuracy, which makes such type of modelling far from being standardized or of black-box type. Although diverse best-practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), no automated procedures are available for all steps of the QM/MM model construction. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on the MM model parametrization, on atom-economical physically-motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build de novo models based on first-principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by some specific chemical or technical limitations.

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“…MATCH was also used to develop what the authors referred to as “bond charge increment rules” used to infer charges for a novel molecule based on connectivity [ 18 ]. CherryPicker [ 19 ] uses a similar graph-based approach focusing on matching fragments from an existing library of building blocks. For example, CherryPicker can be used to assign parameters for molecules with the same chemical functionality and connectivity as peptides.…”

Section: Introductionmentioning

confidence: 99%

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules

Stroet,

Caron,

Engler

et al. 2023

J Comput Aided Mol Des

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An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. Graphical abstract OFraMP applied to paclitaxel (ATB ID 35922).

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CherryPicker: An Algorithm for the Automated Parametrization of Large Biomolecules for Molecular Simulation

Cited by 8 publications

References 38 publications

UniversalQM/MMapproaches for general nanoscale applications

UniversalQM/MMapproaches for general nanoscale applications

Universal QM/MM Approaches for General Nanoscale Applications

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules

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