The Role of Molecular Dynamics Potential of Mean Force Calculations in the Investigation of Enzyme Catalysis

Conformational dynamics of RNA G4C2 and G2C4 repeat expansions causing ALS/FTD using NMR and molecular dynamics studies

G4C2 and G2C4 repeat expansions in chromosome 9 open reading frame 72 (C9orf72) are the most common cause of genetically defined amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), or c9ALS/FTD. The gene is bidirectionally transcribed, producing G4C2 repeats [r(G4C2)exp] and G2C4 repeats [r(G2C4)exp]. The c9ALS/FTD repeat expansions are highly structured, and structural studies showed that r(G4C2)exp predominantly folds into a hairpin with a periodic array of 1 × 1 G/G internal loops and a G-quadruplex. A small molecule probe revealed that r(G4C2)exp also adopts a hairpin structure with 2 × 2 GG/GG internal loops. We studied the conformational dynamics adopted by 2 × 2 GG/GG loops using temperature replica exchange molecular dynamics (T-REMD) and further characterized the structure and underlying dynamics using traditional 2D NMR techniques. These studies showed that the loop's closing base pairs influence both structure and dynamics, particularly the configuration adopted around the glycosidic bond. Interestingly, r(G2C4) repeats, which fold into an array of 2 × 2 CC/CC internal loops, are not as dynamic. Collectively, these studies emphasize the unique sensitivity of r(G4C2)exp to small changes in stacking interactions, which is not observed in r(G2C4)exp, providing important considerations for further principles in structure-based drug design.

Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity

PlaF is a cytoplasmic membrane-bound phospholipase A₁ from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. PlaF activity is regulated by a dimer-to-monomer transition followed by tilting of the monomer in the membrane. However, how substrates reach the active site and how the characteristics of the active site tunnels determine the activity, specificity, and regioselectivity of PlaF for natural GPL substrates have remained elusive. Here, we combined unbiased and biased all-atom molecular dynamics (MD) simulations and configurational free-energy computations to identify access pathways of GPL substrates to the catalytic center of PlaF. Our results map out a distinct tunnel through which substrates access the catalytic center. PlaF variants with bulky tryptophan residues in this tunnel revealed decreased catalysis rates due to tunnel blockage. The MD simulations suggest that GPLs preferably enter the active site with the sn-1 acyl chain first, which agrees with the experimentally demonstrated PLA₁ activity of PlaF. We propose that the acyl chain-length specificity of PlaF is determined by the structural features of the access tunnel, which results in favorable free energy of binding of medium-chain GPLs. The suggested egress route conveys fatty acid (FA) products to the dimerization interface and, thus, contributes to understanding the product feedback regulation of PlaF by FA-triggered dimerization. These findings open up opportunities for developing potential PlaF inhibitors, which may act as antibiotics against P. aeruginosa.

A Computational Perspective on Molecular Recognition by Galectins

This article presents an overview of recent computational studies dedicated to the analysis of binding between galectins and small-molecule ligands. We first present a summary of the most popular simulation techniques adopted for calculating binding poses and binding energies, and then discuss relevant examples reported in the literature for the three main classes of galectins (dimeric, tandem and chimera). We show that simulation of galectin-ligand interactions is a mature field which has proven invaluable for completing and unraveling experimental observations. Future perspectives to further improve the accuracy and cost-effectiveness of existing computational approaches will involve the development of new schemes to account for solvation and entropy effects, which represent the main current limitations to the accuracy of computational results.

Computational Assessment of the Three-Dimensional Configuration of Dissolved Organic Matter Chromophores and Influence on Absorption Spectra

The three-dimensional configuration of dissolved organic matter (DOM) is an important factor in determining the role of DOM in natural and engineered systems, yet there is still considerable uncertainty regarding the formation and potential stability of molecular aggregates within DOM. In this paper, we describe a computational assessment of the three-dimensional configuration of DOM. Specifically, we were interested in evaluating the hypothesis that DOM forms thermodynamically stable molecular aggregates that as a result were potentially shielded from water solvent molecules. Molecular dynamics simulations of DOM model compounds carefully selected based on ultrahigh-resolution mass spectrometry data revealed that, while DOM does indeed form molecular aggregates, the large majority of molecules (especially, O-atom bearing molecules) are solvent accessible. Additionally, these computations revealed that molecular aggregates are weak and dissociate when placed in organic solvents (tetrahydrofuran, methyl tert-butyl ether). Time-dependent density functional theory calculations demonstrated long-wavelength absorbance for both model DOM chromophores and their molecular aggregates. This study has important implications for determining the origin of DOM optical properties and for enhancing our collective understanding of DOM three-dimensional structures.

Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes

With the increase of the need to use more sustainable processes for the industry in our society, the modeling of enzymes has become crucial to fully comprehend their mechanism of action and use this knowledge to enhance and design their properties. A lot of methods to study enzymes computationally exist and they have been classified on sequence-based, structure-based, and the more new artificial intelligence-based ones. Albeit the abundance of methods to help predict the function of an enzyme, molecular modeling is crucial when trying to understand the enzyme mechanism, as they aim to correlate atomistic information with experimental data. Among them, methods that simulate the system dynamics at a molecular mechanics level of theory (classical force fields) have shown to offer a comprehensive study. In this book chapter, we will analyze these techniques, emphasizing the importance of precise modeling of enzyme-substrate interactions. In the end, a brief explanation of the transference of the information from research studies to the industry is given accompanied with two examples of family enzymes where their modeling has helped their exploitation.