METALLOPROTEIN PARAMETERS IN MOLECULAR DYNAMICS SIMULATION FOR AMBER, CHARMM, GROMACS, AND NAMD: A SYSTEMATIC REVIEW

PURNAWAN PONTANA PUTRA; NAJMIATUL FITRIA; AIYI ASNAWI; AKMAL DJAMAAN

doi:10.22159/ijap.2024v16i5.51513

Authors

PURNAWAN PONTANA PUTRA Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0001-9466-4569
NAJMIATUL FITRIA Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia https://orcid.org/0000-0002-3255-1961
AIYI ASNAWI Faculty of Pharmacy, Bhakti Kencana University, Bandung-40614, Indonesia https://orcid.org/0000-0002-8179-0520
AKMAL DJAMAAN Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Andalas, Padang-25163, Indonesia

DOI:

https://doi.org/10.22159/ijap.2024v16i5.51513

Keywords:

Metalloprotein, Parameters, Molecular dynamics, Systematic review

Abstract

Objective: The selection of appropriate metal parameters for molecular dynamics simulations is a significant challenge. Therefore, this review aims to provide in-depth insights valuable for the optimization of parameter selection in the context of chemical simulations.

Methods: A total of 550 scientific articles were collected from pubmed and science direct databases from 2009 to 2024, resulting in the inclusion of 60 full studies for review. The selection process of preferred reporting items for systematic reviews and meta-analyses (PRISMA) was utilized, enabling the conduction of an initial screening of articles by use of the Rayyan web-based application.

Results: This study found that the modeling and parameterization of metal proteins were categorized into bonded and non-bonded models. The Bonded Model incorporates MCPB, a Python-based software that facilitates parameter construction for over 80 metal ions and force fields in molecular dynamics simulations. The non-bonded model evaluates metals in proteins, such as zinc, nickel, magnesium, cobalt, iron, and cadmium by using AMBER force field and the Seminario method. The 12-6 lennard-Jones (LJ) non-bonded model is suitable for divalent, trivalent, and tetravalent metals, with Zinc parameters being compared for accuracy. Additionally, the force fields suitable for modeling unbound metal proteins include AMBER FF19SB, FF14SB, ff9X, CHARMM36, CHARMM22, CHARMM27, and CHARMM-Metal.

Conclusion: This study found that the modeling and parameterization of metal proteins were categorized into bonded and non-bonded models. molecular dynamics (MD) simulations can be conducted using various methods, such as classical molecular dynamics, umbrella sampling, quantum mechanics-discrete molecular dynamics (QM/DMD), stochastic boundary molecular dynamics (SBMD), steered molecular dynamics (SMD), gaussian accelerated molecular dynamics (GaMD) and random acceleration molecular dynamics (RAMD).

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