Coarse-grained molecular dynamics simulations of protein-ligand binding

Kinetic Mechanism of Surfactant-Based Molecular Recognition: Selective Permeability across an Oil-Water Interface Regulated by Supramolecular Aggregates

Lipids are known to play a vital role in the molecular organization of all cellular life. Molecular recognition is another fundamental biological process that is generally attributed to biological polymers, such as proteins and nucleic acids. However, there is evidence that aggregates of lipids and lipid-like molecules are also capable of selectively binding to or regulating the partitioning of other molecules. We previously demonstrated that a model two-phase octanol/water system can selectively partition Red 40 and Blue 1 dyes added to an aqueous phase, with the selectivity depending on the surfactant (e.g., cetyltrimethylammonium bromide) dissolved in the organic phase. Here, we elucidate the mechanism of molecular recognition in this system by using quantitative partitioning experiments and molecular dynamics (MD) simulations. Our results indicate that the selectivity for the red dye is thermodynamically favored at all surfactant concentrations, while selectivity for the blue dye is kinetically favored at high surfactant concentrations. The kinetic selectivity for the blue dye correlates with the presence of molecular aggregation at the oil-water interface. Coarse-grained MD simulations elucidate nanoscale supramolecular structures that can preferentially bind one small molecule rather than another at an interface, providing a selectively permeable barrier in the absence of proteins. The results suggest a new supramolecular mechanism for molecular recognition with potential applications in drug delivery, drug discovery, and biosensing.

Transient binding sites at the surface of haloalkane dehalogenase LinB as locations for fine-tuning enzymatic activity

The haloalkane dehalogenase LinB is a well-known enzyme that contains buried active site and is used for many modelling studies. Using classical molecular dynamics simulations of enzymes and substrates, we searched for transient binding sites on the surface of the LinB protein by calculating maps of enzyme-ligand interactions that were then transformed into sparse matrices. All residues considered as functionally important for enzyme performance (e.g., tunnel entrances) were excluded from the analysis to concentrate rather on non-obvious surface residues. From a set of 130 surface residues, twenty-six were proposed as a promising improvement of enzyme performance. Eventually, based on rational selection and filtering out the potentially unstable mutants, a small library of ten mutants was proposed to validate the possibility of fine-tuning the LinB protein. Nearly half of the predicted mutant structures showed improved activity towards the selected substrates, which demonstrates that the proposed approach could be applied to identify non-obvious yet beneficial mutations for enzyme performance especially when obvious locations have already been explored.

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Carbohydrate-Protein Interactions: Advances and Challenges

A carbohydrate, also called saccharide in biochemistry, is a biomolecule consisting of carbon (C), hydrogen (H) and oxygen (O) atoms. For example, sugars are low molecular-weight carbohydrates, and starches are high molecular-weight carbohydrates. Carbohydrates are the most abundant organic substances in nature and essential constituents of all living things. Protein-carbohydrate interactions play important roles in many biological processes, such as cell growth, differentiation, and aggregation. They also have broad applications in pharmaceutical drug design. In this review, we will summarize the characteristic features of protein-carbohydrate interactions and review the computational methods for structure prediction, energy calculations, and kinetic studies of protein-carbohydrate complexes. Finally, we will discuss the challenges in this field.

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Computer-aided design of protein-ligand binding is important for the development of novel drugs. Here authors present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand binding interactions of small drug-like molecules.

Capturing Protein-Ligand Recognition Pathways in Coarse-Grained Simulation

Protein-ligand recognition is dynamic and complex. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of the ligand in its act of binding to its designated protein cavity. Toward this end, ultralong all-atom molecular dynamics simulation has recently emerged as a popular method of choice because of its ability to record these events at high spatial and temporal resolution. However, success via this route comes at an exorbitant computational cost. Herein, we demonstrate that coarse-grained models of the protein, when systematically optimized to maintain its tertiary fold, can capture the complete process of spontaneous protein-ligand binding from bulk media to the cavity at crystallographic precision and within wall clock time that is orders of magnitude shorter than that of all-atom simulations. The exhaustive sampling of ligand exploration in protein and solvent, harnessed by coarse-grained simulation, leads to elucidation of new ligand recognition pathways and discovery of non-native binding poses.

Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications

Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.

A ternary complex model of Sirtuin4-NAD+-Glutamate dehydrogenase

Sirtuin4 (Sirt4) is one of the mammalian homologues of Silent information regulator 2 (Sir2), which promotes the longevity of yeast, C. elegans, fruit flies and mice. Sirt4 is localized in the mitochondria, where it contributes to preventing the development of cancers and ischemic heart disease through regulating energy metabolism. The ADP-ribosylation of glutamate dehydrogenase (GDH), which is catalyzed by Sirt4, downregulates the TCA cycle. However, this reaction mechanism is obscure, because the structure of Sirt4 is unknown. We here constructed structural models of Sirt4 by homology modeling and threading, and docked nicotinamide adenine dinucleotide⁺ (NAD⁺) to Sirt4. In addition, a partial GDH structure was docked to the Sirt4-NAD⁺ complex model. In the ternary complex model of Sirt4-NAD⁺-GDH, the acetylated lysine 171 of GDH is located close to NAD⁺. This suggests a possible mechanism underlying the ADP-ribosylation at cysteine 172, which may occur through a transient intermediate with ADP-ribosylation at the acetylated lysine 171. These results may be useful in designing drugs for the treatment of cancers and ischemic heart disease.

Calculation of Diffusion Coefficients through Coarse-Grained Simulations Using the Automated-Fragmentation-Parametrization Method and the Recovery of Wilke-Chang Statistical Correlation

We introduce a model for the calculation of diffusion coefficients using dissipative particle dynamics coarse-grained molecular simulations. We validate the model on experimental diffusion data of small organics and drug-like molecules in water. The new model relies on our automated-fragmentation-parametrization protocol for cutting molecules into fragments, which are calibrated using the COSMO-RS thermodynamic model (J. Chem. Inf. Model.2016, 56 ( (12), ), 2361−2377, DOI: 10.1021/acs.jcim.6b0000327806564). By simulations over the entire CULGI database of more than 11000 molecules, we recover the decades-old empirical Wilke–Chang correlation between diffusion coefficient and molar volume. We believe this is the first demonstration of the correlation by simulation or theory. From a comparison of simulated and experimental diffusion coefficients, we find that one full time unit of coarse-grained simulation equals 64 ± 13 ps real time.

"Martinizing" the Variational Implicit Solvent Method (VISM): Solvation Free Energy for Coarse-Grained Proteins

Solvation is a fundamental driving force in many biological processes including biomolecular recognition and self-assembly, not to mention protein folding, dynamics, and function. The variational implicit solvent method (VISM) is a theoretical tool currently developed and optimized to estimate solvation free energies for systems of very complex topology, such as biomolecules. VISM's theoretical framework makes it unique because it couples hydrophobic, van der Waals, and electrostatic interactions as a functional of the solvation interface. By minimizing this functional, VISM produces the solvation interface as an output of the theory. In this work, we push VISM to larger scale applications by combining it with coarse-grained solute Hamiltonians adapted from the MARTINI framework, a well-established mesoscale force field for modeling large-scale biomolecule assemblies. We show how MARTINI-VISM (^MVISM) compares with atomistic VISM (^AVISM) for a small set of proteins differing in size, shape, and charge distribution. We also demonstrate ^MVISM's suitability to study the solvation properties of an interesting encounter complex, barnase-barstar. The promising results suggest that coarse-graining the protein with the MARTINI force field is indeed a valuable step to broaden VISM's and MARTINI's applications in the near future.

Critical residues and motifs for homodimerization of the first transmembrane domain of the plasma membrane glycoprotein CD36

The plasma transmembrane (TM) glycoprotein CD36 is critically involved in many essential signaling processes, especially the binding/uptake of long-chain fatty acids and oxidized low-density lipoproteins. The association of CD36 potentially activates cytosolic protein tyrosine kinases that are thought to associate with the C-terminal cytoplasmic tail of CD36. To understand the mechanisms by which CD36 mediates ligand binding and signal transduction, we have characterized the homo-oligomeric interaction of CD36 TM domains in membrane environments and with molecular dynamics (MD) simulations. Analysis of pyrene- and coumarin-labeled TM1 peptides in SDS by FRET confirmed the homodimerization of the CD36 TM1 peptide. Homodimerization assays of CD36 TM domains with the TOXCAT technique showed that its first TM (TM1) domain, but not the second TM (TM2) domain, could homodimerize in a cell membrane. Small-residue, site-specific mutation scanning revealed that the CD36 TM1 dimerization is mediated by the conserved small residues Gly¹², Gly¹⁶, Ala²⁰, and Gly²³ Furthermore, molecular dynamics (MD) simulation studies demonstrated that CD36 TM1 exhibited a switching dimerization with two right-handed packing modes driven by the ¹²GXXXGXXXA²⁰ and ²⁰AXXG²³ motifs, and the mutational effect of G16I and G23I revealed these representative conformations of CD36 TM1. This packing switch pattern of CD36 TM1 homodimer was further examined and confirmed by FRET analysis of monobromobimane (mBBr)-labeled CD36 TM1 peptides. Overall, this work provides a structural basis for understanding the role of TM association in regulating signal transduction via CD36.

Mechanism of the Association Pathways for a Pair of Fast and Slow Binding Ligands of HIV-1 Protease

Equilibrium constants, together with kinetic rate constants of binding, are key factors in the efficacy and safety of drug compounds, informing drug design. However, the association pathways of protein-ligand binding, which contribute to their kinetic behaviors, are little understood. In this work, we used unbiased all-atom molecular dynamics (MD) simulations with an explicit solvent model to study the association processes of protein-ligand binding. Using the HIV protease (HIVp)-xk263 and HIVp-ritonavir protein-ligand systems as cases, we observed that ligand association is a multistep process involving diffusion, localization, and conformational rearrangements of the protein, ligand, and water molecules. Moreover, these two ligands preferred different routes of binding, which reflect two well-known binding mechanisms: induced-fit and conformation selection models. Our study shows that xk263 has a stronger capacity for desolvating surrounding water molecules, thereby inducing a semiopen conformation of the HIVp flaps (induced-fit model). In contrast, the slow dehydration characteristic of ritonavir allows for gradual association with the binding pocket of HIVp when the protein's flap conformation is fully open (conformation selection model). By studying the mechanism of ligand association and understanding the role of solvent molecules during the binding event, we can obtain a different perspective on the mechanism of macromolecule recognition, providing insights into drug discovery.

Common Internal Allosteric Network Links Anesthetic Binding Sites in a Pentameric Ligand-Gated Ion Channel

General anesthetics bind reversibly to ion channels, modifying their global conformational distributions, but the underlying atomic mechanisms are not completely known. We examine this issue by way of the model protein Gloeobacter violaceous ligand-gated ion channel (GLIC) using computational molecular dynamics, with a coarse-grained model to enhance sampling. We find that in flooding simulations, both propofol and a generic particle localize to the crystallographic transmembrane anesthetic binding region, and that propofol also localizes to an extracellular region shared with the crystallographic ketamine binding site. Subsequent simulations to probe these binding modes in greater detail demonstrate that ligand binding induces structural asymmetry in GLIC. Consequently, we employ residue interaction correlation analysis to describe the internal allosteric network underlying the coupling of ligand and distant effector sites necessary for conformational change. Overall, the results suggest that the same allosteric network may underlie the actions of various anesthetics, regardless of binding site.

Enhanced sampling simulations to construct free-energy landscape of protein-partner substrate interaction

Molecular dynamics (MD) simulations using all-atom and explicit solvent models provide valuable information on the detailed behavior of protein-partner substrate binding at the atomic level. As the power of computational resources increase, MD simulations are being used more widely and easily. However, it is still difficult to investigate the thermodynamic properties of protein-partner substrate binding and protein folding with conventional MD simulations. Enhanced sampling methods have been developed to sample conformations that reflect equilibrium conditions in a more efficient manner than conventional MD simulations, thereby allowing the construction of accurate free-energy landscapes. In this review, we discuss these enhanced sampling methods using a series of case-by-case examples. In particular, we review enhanced sampling methods conforming to trivial trajectory parallelization, virtual-system coupled multicanonical MD, and adaptive lambda square dynamics. These methods have been recently developed based on the existing method of multicanonical MD simulation. Their applications are reviewed with an emphasis on describing their practical implementation. In our concluding remarks we explore extensions of the enhanced sampling methods that may allow for even more efficient sampling.

United polarizable multipole water model for molecular mechanics simulation

We report the development of a united AMOEBA (uAMOEBA) polarizable water model, which is computationally 3-5 times more efficient than the three-site AMOEBA03 model in molecular dynamics simulations while providing comparable accuracy for gas-phase and liquid properties. In this coarse-grained polarizable water model, both electrostatic (permanent and induced) and van der Waals representations have been reduced to a single site located at the oxygen atom. The permanent charge distribution is described via the molecular dipole and quadrupole moments and the many-body polarization via an isotropic molecular polarizability, all located at the oxygen center. Similarly, a single van der Waals interaction site is used for each water molecule. Hydrogen atoms are retained only for the purpose of defining local frames for the molecular multipole moments and intramolecular vibrational modes. The parameters have been derived based on a combination of ab initio quantum mechanical and experimental data set containing gas-phase cluster structures and energies, and liquid thermodynamic properties. For validation, additional properties including dimer interaction energy, liquid structures, self-diffusion coefficient, and shear viscosity have been evaluated. The results demonstrate good transferability from the gas to the liquid phase over a wide range of temperatures, and from nonpolar to polar environments, due to the presence of molecular polarizability. The water coordination, hydrogen-bonding structure, and dynamic properties given by uAMOEBA are similar to those derived from the all-atom AMOEBA03 model and experiments. Thus, the current model is an accurate and efficient alternative for modeling water.