Mechanisms Underlying Proton Release in CLC-type F-/H+ Antiporters

Multiscale biomolecular simulations in the exascale era

The complexity of biological systems and processes, spanning molecular to macroscopic scales, necessitates the use of multiscale simulations to get a comprehensive understanding. Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations are crucial for capturing processes beyond the reach of classical MD simulations. The advent of exascale computing offers unprecedented opportunities for scientific exploration, not least within life sciences, where simulations are essential to unravel intricate molecular mechanisms underlying biological processes. However, leveraging the immense computational power of exascale computing requires innovative algorithms and software designs. In this context, we discuss the current status and future prospects of multiscale biomolecular simulations on exascale supercomputers with a focus on QM/MM MD. We highlight our own efforts in developing a versatile and high-performance multiscale simulation framework with the aim of efficient utilization of state-of-the-art supercomputers. We showcase its application in uncovering complex biological mechanisms and its potential for leveraging exascale computing.

Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations

The rate constants of enzyme-catalyzed reactions (kcat) are often approximated from the barrier height of the reactive step. We introduce an enhanced sampling QM/MM approach that directly calculates the kinetics of enzymatic reactions, without introducing the transition-state theory assumptions, and takes into account the dynamical equilibrium between the reactive and non-reactive conformations of the enzyme/substrate complex. Our computed kcat values are in order-of-magnitude agreement with the experimental data for two representative enzymatic reactions.

Fluoride Ion Binding and Translocation in the CLCF Fluoride/Proton Antiporter: Molecular Insights from Combined Quantum-Mechanical/Molecular-Mechanical Modeling

CLCF fluoride/proton antiporters move fluoride ions out of bacterial cells, leading to fluoride resistance in these bacteria. However, many details about their operating mechanisms remain unclear. Here, we report a combined quantum-mechanical/molecular-mechanical (QM/MM) study of a CLCF homologue from Enterococci casseliflavus (Eca), in accord with the previously proposed windmill mechanism. Our multiscale modeling sheds light on two critical steps in the transport cycle: (i) the external gating residue E118 pushing a fluoride in the external binding site into the extracellular vestibule and (ii) an incoming fluoride reconquering the external binding site by forcing out E118. Both steps feature competitions for the external binding site between the negatively charged carboxylate of E118 and the fluoride. Remarkably, the displaced E118 by fluoride accepts a proton from the nearby R117, initiating the next transport cycle. We also demonstrate the importance of accurate quantum descriptions of fluoride solvation. Our results provide clues to the mysterious E318 residue near the central binding site, suggesting that the transport activities are unlikely to be disrupted by the glutamate interacting with a well-solvated fluoride at the central binding site. This differs significantly from the structurally similar CLC chloride/proton antiporters, where a fluoride trapped deep in the hydrophobic pore causes the transporter to be locked down. A free-energy barrier of 10-15 kcal/mol was estimated via umbrella sampling for a fluoride ion traveling through the pore to repopulate the external binding site.

Computational approaches to investigate fluoride binding, selectivity and transport across the membrane

The use of molecular dynamics (MD) simulations to study biomolecular systems has proven reliable in elucidating atomic-level details of structure and function. In this chapter, MD simulations were used to uncover new insights into two phylogenetically unrelated bacterial fluoride (F-) exporters: the CLCF F-/H+ antiporter and the Fluc F- channel. The CLCF antiporter, a member of the broader CLC family, has previously revealed unique stoichiometry, anion-coordinating residues, and the absence of an internal glutamate crucial for proton import in the CLCs. Through MD simulations enhanced with umbrella sampling, we provide insights into the energetics and mechanism of the CLCF transport process, including its selectivity for F- over HF. In contrast, the Fluc F- channel presents a novel architecture as a dual topology dimer, featuring two pores for F- export and a central non-transported sodium ion. Using computational electrophysiology, we simulate the electrochemical gradient necessary for F- export in Fluc and reveal details about the coordination and hydration of both F- and the central sodium ion. The procedures described here delineate the specifics of these advanced techniques and can also be adapted to investigate other membrane protein systems.

Fluoride Transport and Inhibition Across CLC Transporters

The Chloride Channel (CLC) family includes proton-coupled chloride and fluoride transporters. Despite their similar protein architecture, the former exchange two chloride ions for each proton and are inhibited by fluoride, whereas the latter efficiently transport one fluoride in exchange for one proton. The combination of structural, mutagenesis, and functional experiments with molecular simulations has pinpointed several amino acid changes in the permeation pathway that capitalize on the different chemical properties of chloride and fluoride to fine-tune protein function. Here we summarize recent findings on fluoride inhibition and transport in the two prototypical members of the CLC family, the chloride/proton transporter from Escherichia coli (CLC-ec1) and the fluoride/proton transporter from Enterococcus casseliflavus (CLCF-eca).

Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations

The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.

Uncovering the Mechanism of the Proton-Coupled Fluoride Transport in the CLCF Antiporter

Fluoride is a natural antibiotic abundantly present in the environment and, in micromolar concentrations, is able to inhibit enzymes necessary for bacteria to survive. However, as is the case with many antibiotics, bacteria have evolved resistance methods, including through the use of recently discovered membrane proteins. One such protein is the CLC^F F^-/H⁺ antiporter protein, a member of the CLC superfamily of anion-transport proteins. Though previous studies have examined this F^- transporter, many questions are still left unanswered. To reveal details of the transport mechanism used by CLC^F, we have employed molecular dynamics simulations and umbrella sampling calculations. Our results have led to several discoveries, including the mechanism of proton import and how it is able to aid in the fluoride export. Additionally, we have determined the role of the previously identified residues Glu118, Glu318, Met79, and Tyr396. This work is among the first studies of the CLC^F F^-/H⁺ antiporter and is the first computational investigation to model the full transport process, proposing a mechanism which couples the F^- export with the H⁺ import.

MiMiCPy: An Efficient Toolkit for MiMiC-Based QM/MM Simulations

MiMiC is a highly flexible, extremely scalable multiscale modeling framework. It couples the CPMD (quantum mechanics, QM) and GROMACS (molecular mechanics, MM) codes. The code requires preparing separate input files for the two programs with a selection of the QM region. This can be a tedious procedure prone to human error, especially when dealing with large QM regions. Here, we present MiMiCPy, a user-friendly tool that automatizes the preparation of MiMiC input files. It is written in Python 3 with an object-oriented approach. The main subcommand PrepQM can be used to generate MiMiC inputs directly from the command line or through a PyMOL/VMD plugin for visually selecting the QM region. Many other subcommands are also provided for debugging and fixing MiMiC input files. MiMiCPy is designed with a modular structure that allows seamless extensions to new program formats depending on the requirements of MiMiC.

Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of ab initio molecular dynamics to explore and characterize the reaction space.