Abstract
By directly affecting structure, dynamics and interaction networks of their targets, post-translational modifications (PTMs) of proteins play a key role in different cellular processes ranging from enzymatic activation to regulation of signal transduction to cell-cycle control. Despite the great importance of understanding how PTMs affect proteins at the atomistic level, a systematic framework for treating post-translationally modified amino acids by molecular dynamics (MD) simulations, a premier high-resolution computational biology tool, has never been developed. Here, we report and validate force field parameters (GROMOS 45a3 and 54a7) required to run and analyze MD simulations of more than 250 different types of enzymatic and non-enzymatic PTMs. The newly developed GROMOS 54a7 parameters in particular exhibit near chemical accuracy in matching experimentally measured hydration free energies (RMSE = 4.2 kJ/mol over the validation set). Using this tool, we quantitatively show that the majority of PTMs greatly alter the hydrophobicity and other physico-chemical properties of target amino acids, with the extent of change in many cases being comparable to the complete range spanned by native amino acids.
Highlights
Proteins in the cell continually get covalently modified in different post-translational, enzyme-controlled reactions [1,2,3]
We develop and validate force field parameters, an essential part of the molecular dynamics method, for more than 250 different types of enzymatic and non-enzymatic post-translational modifications
The parameters presented in this study greatly expand the range of applicability of computational methods, and in particular molecular dynamics simulations, to a large set of new systems with utmost biological and biomedical importance
Summary
Proteins in the cell continually get covalently modified in different post-translational, enzyme-controlled reactions [1,2,3]. MD simulations capture atomic and molecular motions based on Newton’s equation of motion and an empirical potential energy function that defines interactions between simulated particles. The latter is defined by a force field, i.e. a self-consistent set of physically realistic equations and semiempirical parameters describing all interactions in a given system. We develop force field parameters for over 250 different types of enzymatic and non-enzymatic modifications of amino-acid side chains as well as protein termini within the context of GROMOS 45a3 [19] and 54a7 [21,22] force fields (Table S1). The functional form of a typical force field is exemplified in equation 1 for GROMOS class force fields, Epot ~
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