Protein Backbone Dynamics Simulations Using Coarse-Grained Bonded Potentials and Simplified Hydrogen Bonds

Probing the hierarchical dynamics of DNA-sperm nuclear transition protein complexes through fuzzy interaction and mesoscale condensation

Nuclear transition protein TNP1 is a crucial player mediating histone-protamine exchange in condensing spermatids. A unique combination of intrinsic disorder and multivalent properties turns TNP1 into an ideal agent for orchestrating the formation of versatile TNP-DNA assemblies. Despite its significance, the physicochemical property and the molecular mechanism followed by TNP1 for histone replacement and DNA condensation are still poorly understood. This study reports the first-time in vitro expression and purification of human TNP1 and investigates the hierarchical dynamics of TNP1-DNA interaction using a combination of computational simulations, biochemical assays, fluorescence imaging, and atomic force microscopy. We explored three crucial facets of TNP1-DNA interactions. Initially, we delve into the molecular binding process that entails fuzzy interactions between TNP1 and DNA at the atomistic scale. Subsequently, we analyze how TNP1 binding affects the electrostatic and mechanical characteristics of DNA and influences its morphology. Finally, we study the biomolecular condensation of TNP1-DNA when subjected to high concentrations. The findings of our study set the foundation for comprehending the potential involvement of TNP1 in histone replacement and DNA condensation in spermatogenesis.

Two-bead polarizable water models combined with a two-bead multipole force field (TMFF) for coarse-grained simulation of proteins

(a) Four water molecules contained in the polarizable CG water models in (b) two-bead polarizable water model 1 (TPW1) and (c) two-bead polarizable water model 2 (TPW2).

Parametrization of Backbone Flexibility in a Coarse-Grained Force Field for Proteins (COFFDROP) Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of All Possible Two-Residue Peptides

Recently, we reported the parametrization of a set of coarse-grained (CG) nonbonded potential functions, derived from all-atom explicit-solvent molecular dynamics (MD) simulations of amino acid pairs and designed for use in (implicit-solvent) Brownian dynamics (BD) simulations of proteins; this force field was named COFFDROP (COarse-grained Force Field for Dynamic Representations Of Proteins). Here, we describe the extension of COFFDROP to include bonded backbone terms derived from fitting to results of explicit-solvent MD simulations of all possible two-residue peptides containing the 20 standard amino acids, with histidine modeled in both its protonated and neutral forms. The iterative Boltzmann inversion (IBI) method was used to optimize new CG potential functions for backbone-related terms by attempting to reproduce angle, dihedral, and distance probability distributions generated by the MD simulations. In a simple test of the transferability of the extended force field, the angle, dihedral, and distance probability distributions obtained from BD simulations of 56 three-residue peptides were compared to results from corresponding explicit-solvent MD simulations. In a more challenging test of the COFFDROP force field, it was used to simulate eight intrinsically disordered proteins and was shown to quite accurately reproduce the experimental hydrodynamic radii (Rhydro), provided that the favorable nonbonded interactions of the force field were uniformly scaled downward in magnitude. Overall, the results indicate that the COFFDROP force field is likely to find use in modeling the conformational behavior of intrinsically disordered proteins and multidomain proteins connected by flexible linkers.

Minimalist Model for the Dynamics of Helical Polypeptides: A Statistic-Based Parametrization

Low-resolution models are often used to address macroscopic time and size scales in molecular dynamics simulations of biomolecular systems. Coarse graining is often coupled to knowledge-based parametrization to obtain empirical potentials able to reproduce the system thermodynamic behavior. Here, a minimalist coarse grained (GC) model for the helical structures of proteins is reported. A knowledge-based parametrization strategy is coupled to the explicit inclusion of hydrogen-bonding-related terms, resulting in an accurate reproduction of the structure and dynamics of each single helical type, as well as the internal conformational variables correlation. The proposed strategy of basing the force field terms on real physicochemical interactions is transferable to different secondary structures. Thus, this work, though conclusive for helices, is to be considered the first of a series devoted to the application of the knowledge-based, physicochemical model to extended secondary structures and unstructured proteins.

Coarse-grained models of the proteins backbone conformational dynamics

Coarse-grained models are more and more frequently used in the studies of the proteins structural and dynamic properties, since the reduced number of degrees of freedom allows to enhance the conformational space exploration. This chapter attempts to provide an overview of the various coarse-grained models that were applied to study the functional conformational changes of the polypeptides main chain around their native state. It will more specifically discuss the methods used to represent the protein backbone flexibility and to account for the physico-chemical interactions that stabilize the secondary structure elements.

Coarse-grained models for protein folding and aggregation

Coarse-grained models for protein folding and aggregation are used to explore large dimension scales and timescales that are inaccessible to all-atom models in explicit aqueous solution. Combined with enhanced configuration search methods, these simplified models with various levels of granularity offer the possibility to determine equilibrium structures, compare folding kinetics and thermodynamics with experiments for single proteins and understand the dynamic assembly of amyloid proteins leading to neurodegenerative diseases. I shall describe recent progress in developing such models, and discuss their potentials and limitations in probing the folding and misfolding of proteins with computer simulations.

The Martini coarse-grained force field

The Martini force field is a coarse-grained force field suited for molecular dynamics simulations of biomolecular systems. The force field has been parameterized in a systematic way, based on the reproduction of partitioning free energies between polar and apolar phases of a large number of chemical compounds. In this chapter the methodology underlying the force field is presented together with details of its parameterization and limitations. Then currently available topologies are described with a short overview of the key elements of their parameterization. These include the new polarizable Martini water model. A set of three selected ongoing studies using the Martini force field is presented. Finally the latest lines of development are discussed.

Modeling Protein-Protein Recognition in Solution Using the Coarse-Grained Force Field SCORPION

We present here the SCORPION-Solvated COaRse-grained Protein interactION-force field, a physics-based simplified coarse-grained (CG) force field. It combines our previous CG protein model and a novel particle-based water model which makes it suitable for Molecular Dynamics (MD) simulations of protein association processes. The protein model in SCORPION represents each amino acid with one to three beads, for which electrostatic and van der Waals effective interactions are fitted separately to reproduce those of the all-atom AMBER force field. The protein internal flexibility is accounted for by an elastic network model (ENM). We now include in SCORPION a new Polarizable Coarse-Grained Solvent (PCGS) model, which is computationally efficient, consistent with the protein CG representation, and yields accurate electrostatic free energies of proteins. SCORPION is used here for the first time to perform hundreds-of-nanoseconds-long MD simulations of protein/protein recognition in water, here the case of the barnase/barstar complex. These MD simulations showed that, for five of a total of seven simulations starting from several initial conformations, and after a time going from 1 to 500 ns, the proteins bind in a conformation very close to the native bound structure and remain stable in this conformation for the rest of the simulation. An energetic analysis of these MD show that this recognition is driven both by van der Waals and electrostatic interactions between proteins. SCORPION appears therefore as a useful tool to study protein-protein recognition in a solvated environment.

A generic force field for protein coarse-grained molecular dynamics simulation

Coarse-grained (CG) force fields have become promising tools for studies of protein behavior, but the balance of speed and accuracy is still a challenge in the research of protein coarse graining methodology. In this work, 20 CG beads have been designed based on the structures of amino acid residues, with which an amino acid can be represented by one or two beads, and a CG solvent model with five water molecules was adopted to ensure the consistence with the protein CG beads. The internal interactions in protein were classified according to the types of the interacting CG beads, and adequate potential functions were chosen and systematically parameterized to fit the energy distributions. The proposed CG force field has been tested on eight proteins, and each protein was simulated for 1000 ns. Even without any extra structure knowledge of the simulated proteins, the Cα root mean square deviations (RMSDs) with respect to their experimental structures are close to those of relatively short time all atom molecular dynamics simulations. However, our coarse grained force field will require further refinement to improve agreement with and persistence of native-like structures. In addition, the root mean square fluctuations (RMSFs) relative to the average structures derived from the simulations show that the conformational fluctuations of the proteins can be sampled.

Coarse-grain Protein Models

Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.

Implementation of a protein reduced point charge model toward molecular dynamics applications

A reduced point charge model was developed in a previous work from the study of extrema in smoothed charge density distribution functions generated from the Amber99 molecular electrostatic potential. In the present work, such a point charge distribution is coupled with the Amber99 force field and implemented in the program TINKER to allow molecular dynamics (MD) simulations of proteins. First applications to two polypeptides that involve α-helix and β-sheet motifs are analyzed and compared to all-atom MD simulations. Two types of coarse-grained (CG)-based trajectories are generated using, on one hand, harmonic bond stretching terms and, on the other hand, distance restraints. Results show that the use of the unrestrained CG conditions are sufficient to preserve most of the secondary structure characteristics but restraints lead to a better agreement between CG and all-atom simulation results such as rmsd, dipole moment, and time-dependent mean square deviation functions.

Vibrational dynamics of hydrogen in proteins

Biological macromolecules expand with increasing temperature and this dynamic expansion is associated with the onset of function. The expansion is typically characterized by the mean square vibrational displacement (MSD), <u²> of specific constituents such as hydrogen within the macromolecules. The <u²> increases with increasing temperature and the slope of <u²> versus temperature can increase significantly at a temperature T{D} identified as a dynamical transition. We illustrate that the observed expansion and change in slope of <u²> with temperature at T{D} can be reproduced within a simple model of the vibration, an atom in an anharmonic potential, V(u). Given V(u), only the temperature is varied in the model. A simple Gaussian potential or a potential containing a hard wall is particularly effective is reproducing the observed change in the slope of <u²> with temperature around T{D}.

Minimalist models for proteins: a comparative analysis

The last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex. In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid. From this analysis several issues emerge. The difficulty in building these models resides in the need for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.