Molecular dynamics simulations of an α-synuclein NAC domain fragment with a ff14IDPSFF IDP-specific force field suggest ß-sheet intermediate states of fibrillation.

For the discovery of treatments against synucleinopathies, it is necessary to unravel and fully understand the mechanism of fibrillation of proteins involved. Among them, α-synuclein (αS) plays a key role in the development of these diseases through its aggregation into oligomers found in Lewy bodies. However, its structural disorder as an intrinsically disordered protein (IDP) makes its characterization by experimental techniques arduously difficult. Atomistic simulations aim to provide insights into this blank canvas and, fortunately, some studies have already suggested promising mechanisms. Still, it is urgent to consider the IDP features in simulations, so recently a lot of force fields designed to deal with IDPs have been developed. In this study, we have carried out a total of 12 µs simulations of an αS core fragment using a popular ff14SB AMBER force field and the ff14IDPSFF variation that includes a grid-based energy correction map (CMAP) method. The predicted chemical shifts from the simulations and those measured from the αS protein in the NMR solution indicate that ff14IDPSFF reproduces the experimental data more accurately. Moreover, structural analysis exhibits opposite trends between secondary structure propensities. The ff14SB force field preserves the α-helices found in the micelle-bound αS structure, which is used as an initial conformation, while ff14IDPSFF stands out with increased structural disorder and the formation of ß-sheets, which suggests that the IDP-specific force field can capture more suitable conformations representing the possible intermediate states of the fibrillation process.

Unravelling Constant pH Molecular Dynamics in Oligopeptides with Explicit Solvation Model.

An accurate description of the protonation state of amino acids is essential to correctly simulate the conformational space and the mechanisms of action of proteins or other biochemical systems. The pH and the electrochemical environments are decisive factors to define the effective pKa of amino acids and, therefore, the protonation state. However, they are poorly considered in Molecular Dynamics (MD) simulations. To deal with this problem, constant pH Molecular Dynamics (cpHMD) methods have been developed in recent decades, demonstrating a great ability to consider the effective pKa of amino acids within complex structures. Nonetheless, there are very few studies that assess the effect of these approaches in the conformational sampling. In a previous work of our research group, we detected strengths and weaknesses of the discrete cpHMD method implemented in AMBER when simulating capped tripeptides in implicit solvent. Now, we progressed this assessment by including explicit solvation in these peptides. To analyze more in depth the scope of the reported limitations, we also carried out simulations of oligopeptides with distinct positions of the titratable amino acids. Our study showed that the explicit solvation model does not improve the previously noted weaknesses and, furthermore, the separation of the titratable amino acids in oligopeptides can minimize them, thus providing guidelines to improve the conformational sampling in the cpHMD simulations.

Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites.

Diverse computational methods to support fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy in supporting FBDD campaigns, they exhibit some drawbacks such as protein denaturation or ligand aggregation that have not yet been clearly overcome in the framework of biomolecular simulations. In the present work, we discuss a systematic semi-automatic novel computational procedure, designed to surpass these difficulties. The method, named fragment dissolved Molecular Dynamics (fdMD), utilizes simulation boxes of solvated small fragments, adding a repulsive Lennard-Jones potential term to avoid aggregation, which can be easily used to solvate the targets of interest. This method has the advantage of solvating the target with a low number of ligands, thus preventing the denaturation of the target, while simultaneously generating a database of ligand-solvated boxes that can be used in further studies. A number of scripts are made available to analyze the results and obtain the descriptors proposed as a means to trustfully discard spurious binding sites. To test our method, four test cases of different complexity have been solvated with ligand boxes and four molecular dynamics runs of 200 ns length have been run for each system, which have been extended up to 1 µs when needed. The reported results point out that the selected number of replicas are enough to identify the correct binding sites irrespective of the initial structure, even in the case of proteins having several close binding sites for the same ligand. We also propose a set of descriptors to analyze the results, among which the average MMGBSA and the average KDEEP energies have emerged as the most robust ones.

On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides.

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.