Secondary structure dependence on simulation techniques and force field parameters: from disordered to ordered proteins

We describe a process to build and simulate coarse-grained oligomers using temperature replica exchange molecular dynamics and analyze them for thermodynamic and structural characteristics of cooperative folding transitions. We also introduce a Python package (cg_openmm) to carry out these simulations and analyses. We demonstrate the capabilities of cg_openmm on a simple helix-forming homo-oligomer, systematically varying sets of force field parameters and studying the effects on folding cooperativity and helix stability. We find that small changes to force field parameters in the homo-oligomer model can dramatically affect cooperativity, stability, and even lead to helix-to-helix transitions. This software package enables large-scale screening of potential foldamer molecules and will be highly useful in the broader effort of understanding secondary structure formation in terms of non-chemically specific features of molecular models.

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Using a Coarse-Grained Modeling Framework to Identify Oligomeric Motifs with Tunable Secondary Structure

10.26434/chemrxiv.14687463 ◽

2021 ◽

Author(s):

Christopher Walker ◽

Garrett Meek ◽

Theodore Fobe ◽

Michael R. Shirts

Keyword(s):

Secondary Structure ◽

Force Field ◽

Large Scale ◽

Structural Characteristics ◽

Coarse Grained ◽

Molecular Models ◽

Modeling Framework ◽

Force Field Parameters ◽

Python Package ◽

Large Scale Screening

We describe a process to build and simulate coarse-grained oligomers using temperature replica exchange molecular dynamics and analyze them for thermodynamic and structural characteristics of cooperative folding transitions. We also introduce a Python package (cg_openmm) to carry out these simulations and analyses. We demonstrate the capabilities of cg_openmm on a simple helix-forming homo-oligomer, systematically varying sets of force field parameters and studying the effects on folding cooperativity and helix stability. We find that small changes to force field parameters in the homo-oligomer model can dramatically affect cooperativity, stability, and even lead to helix-to-helix transitions. This software package enables large-scale screening of potential foldamer molecules and will be highly useful in the broader effort of understanding secondary structure formation in terms of non-chemically specific features of molecular models.

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QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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RNA secondary structure dependence in METTL3–METTL14 mRNA methylation is modulated by the N-terminal domain of METTL3

Biological Chemistry ◽

10.1515/hsz-2020-0265 ◽

2020 ◽

Vol 402 (1) ◽

pp. 89-98

Author(s):

Nathalie Meiser ◽

Nicole Mench ◽

Martin Hengesbach

Keyword(s):

Secondary Structure ◽

Rna Binding ◽

Specific Protein ◽

Structure Dependence ◽

Terminal Domain ◽

Methylation Assay ◽

A Site ◽

Methylation Activity

AbstractN6-methyladenosine (m6A) is the most abundant modification in mRNA. The core of the human N6-methyltransferase complex (MTC) is formed by a heterodimer consisting of METTL3 and METTL14, which specifically catalyzes m6A formation within an RRACH sequence context. Using recombinant proteins in a site-specific methylation assay that allows determination of quantitative methylation yields, our results show that this complex methylates its target RNAs not only sequence but also secondary structure dependent. Furthermore, we demonstrate the role of specific protein domains on both RNA binding and substrate turnover, focusing on postulated RNA binding elements. Our results show that one zinc finger motif within the complex is sufficient to bind RNA, however, both zinc fingers are required for methylation activity. We show that the N-terminal domain of METTL3 alters the secondary structure dependence of methylation yields. Our results demonstrate that a cooperative effect of all RNA-binding elements in the METTL3–METTL14 complex is required for efficient catalysis, and that binding of further proteins affecting the NTD of METTL3 may regulate substrate specificity.

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