On the Validity of Hydrogen Mass Repartitioning for CHARMM36 Membrane Systems in NAMD

Abstract

exchange, ReMDFF, provide a precise tool to fit atomic structures of a protein inside an electron density map to improve the resolution of the protein structure. Typically, MDFF incorporates the electron microscopy (EM) data as an external potential (U EM ) added to the standard molecular dynamics force field, such that the high-density areas in the map correspond to energy minima, where the atoms are subject to forces proportional to the gradient of the EM map. However, the error involved in biasing the standard potential with U EM has not been explored yet. Re-casting U EM in an integro-differential form, reveals a hidden error energy term reminiscent of long-range interactions between stationary masses. This additional contribution to the force filed unnecessarily contorts the molecular geometries during an MDFF refinement. We perform, a quantitative assessment of the effect of this hidden error term in MDFF's grid force field using recently published structures using EM to Protein Data Bank, of varying residues, 200 -20,000 with 3-8 Å resolution and their corresponding electron density maps. The initial systems are prepared using default MDFF settings, for different grid force scaling parameter. The protein structure files are modified by replacing the charge with atoms mass multiplied by a factor proportional to the error term. The mass-based coulomb like potential is then calculated using NAMD Energy where the structures corresponding to low and high error term values are used and run through MolProbity for comparison of their relative stabilities. Our results indicate, re-weighting an ensemble of structures based on the error-energy term, provides improvement in determining both stereochemical and thermodynamic correct structures of proteins from electron density maps. . The enormous improvements in computational power in recent years means that we can routinely runs simulations of biologically interesting systems out to the microsecond scale or longer. However, in many cases their scientific impact is limited by an inability to extract useful information from this wealth of data. We created LOOS to unlock this previously unrealized potential. Molecular dynamics is unique among biophysical techniques in that the raw data can be interrogated in a multitude of ways; getting the most out of the data usually requires exploration and iterative refinement. LOOS is designed to make this process easy and efficient, to minimize the friction between a user and her data. LOOS can be viewed in two ways: 1) It is a suite of well-validated, easy-to-use tools for analyzing molecular dynamics simulations. These tools include common tasks, such as trajectory manipulation, as well as more advanced techniques such as analysis of simulation convergence, 3D histograms, and Voronoi decomposition, with a particular focus on membranes and membrane-bound proteins. 2) It is a tool for making tools. At its heart, LOOS is a Cþþ library covered with a Python interface, for the optimal mix of high performance and ease of development. L...