MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing

Abstract

Modeling biomolecular assemblies is an important field in computational structural biology. The inherent complexity of their energy landscape and the computational cost associated with modeling large and complex assemblies are major drawbacks for integrative modeling approaches. The so-called coarse-graining approaches, which reduce the degrees of freedom of the system by grouping several atoms into larger “pseudo-atoms,” have been shown to alleviate some of those limitations, facilitating the identification of the global energy minima assumed to correspond to the native state of the complex, while making the calculations more efficient. Here, we describe and assess the implementation of the MARTINI force field for DNA into HADDOCK, our integrative modeling platform. We combine it with our previous implementation for protein-protein coarse-grained docking, enabling coarse-grained modeling of protein-nucleic acid complexes. The system is modeled using MARTINI topologies and interaction parameters during the rigid body docking and semi-flexible refinement stages of HADDOCK, and the resulting models are then converted back to atomistic resolution by an atom-to-bead distance restraints-guided protocol. We first demonstrate the performance of this protocol using 44 complexes from the protein-DNA docking benchmark, which shows an overall ~6-fold speed increase and maintains similar accuracy as compared to standard atomistic calculations. As a proof of concept, we then model the interaction between the PRC1 and the nucleosome (a former CAPRI target in round 31), using the same information available at the time the target was offered, and compare all-atom and coarse-grained models.

Protein–ligand binding with the coarse-grained Martini model

“…Given the improvements in accuracy in Martini 3, another key advantage of CG models in general is their computational performance. For instance, Martini CG based docking of biomolecular complexes can be around one order of magnitude faster compared to atomistic models, as recently demonstrated for the Haddock program 79,80 . Benchmarks tests performed with the program package Gromacs (version 2018) 81 showed that Martini based MD simulations of protein-ligand systems can be 110-350 times faster than all-atom simulations, with the performance gain increasing with growing system size (see Supplementary Discussion and Supplementary Table 2).…”

Coarse-grained nucleic acid–protein model for hybrid nanotechnology

“…However, the oxDNA/oxRNA models only allow for representation of nucleic acids alone, limiting their scope of usability. While there have been coarse-grained simulation models developed for protein-DNA interactions, [31][32][33][34][35][36][37] none are able to be directly used with the oxDNA model. The development of an efficient tool compatible with oxDNA would allow for efficient study of arbitrary protein-DNA complexes.…”

Exploring the most stable aptamer/target molecule complex by the stochastic tunnelling-basin hopping-discrete molecular dynamics method

“…Compared with the structures predicted by the all atom model, it has been confirmed that the MARTINI force field not only accelerates the simulation process by about three orders, but also preserves the characteristics of different amino acids, as the all atom model also does. The comparisons between the structures predicted by the MARTINI CG and all atom models can be also seen for a peptide-bilayer system 32 , DNA 33 , and DNA–protein complexes 34 , which show good reproductively of the CG model to those by the AA model.…”