Binding Free Energy Landscapes Research Articles

Marine Brown Algae-Derived Compounds as Potential Inhibitors of Japanese Encephalitis Virus RNA-Dependent RNA Polymerase.

The Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus that primarily affects people in Asia and seriously threatens public health. Considering the rising occurrence rates and lack of targeted antiviral treatments, it is essential to comprehend and tackle obstacles related to JEV in order to lessen its influence on world health. This investigation explores compounds derived from marine brown algae (Phaeophyceae) as potential inhibitors of JEV RNA-dependent RNA polymerase (RdRp), a critical enzyme in the virus's replication cycle. Employing the computational virtual screen approach, four compounds, i.e., CMNPD16749, CMNPD2606, CMNPD27817, and CMNPD23662, with favorable binding energies ranging from -15.7 Kcal/mol to -13.9 kcal/mol were identified. Subsequently, through molecular docking analysis, the interactions responsible for the binding stability between the target protein and hit molecules compared to the reference molecule Galidesvir were studied. Further, through extensive molecular dynamic (MD) simulation studies at 200 ns, it was confirmed that each docked complex showed acceptable dynamic stability compared to the reference molecule. These findings were further validated using MM/PBSA free binding energy calculations, PCA analysis and free energy landscape construction. These computational findings suggested that the brown algae-derived compounds may act as an antiviral drug against JEV infection and lay a crucial foundation for future experimental studies against JEV.

Studying specificity in protein-glycosaminoglycan recognition with umbrella sampling.

In the past few decades, glycosaminoglycan (GAG) research has been crucial for gaining insights into various physiological, pathological, and therapeutic aspects mediated by the direct interactions between the GAG molecules and diverse proteins. The structural and functional heterogeneities of GAGs as well as their ability to bind specific proteins are determined by the sugar composition of the GAG, the size of the GAG chains, and the degree and pattern of sulfation. A deep understanding of the interactions in protein-GAG complexes is essential to explain their biological functions. In this study, the umbrella sampling (US) approach is used to pull away a GAG ligand from the binding site and then pull it back in. We analyze the binding interactions between GAGs of three types (heparin, desulfated heparan sulfate, and chondroitin sulfate) with three different proteins (basic fibroblast growth factor, acidic fibroblast growth factor, and cathepsin K). The main focus of our study was to evaluate whether the US approach is able to reproduce experimentally obtained structures, and how useful it can be for getting a deeper understanding of GAG properties, especially protein recognition specificity and multipose binding. We found that the binding free energy landscape in the proximity of the GAG native binding pose is complex and implies the co-existence of several binding poses. The sliding of a GAG chain along a protein surface could be a potential mechanism of GAG particular sequence recognition by proteins.

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study.

In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, we apply here two different machine learning (ML) algorithms, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, constituted by a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.

Replica Exchange with Hybrid Tempering Efficiently Samples PGLa Peptide Binding to Anionic Bilayer.

We evaluated the utility of a variant of the replica exchange method, a replica exchange with hybrid tempering (REHT), for all-atom explicit water biomolecular simulations and compared it with a more traditional replica exchange with the solute tempering (REST) algorithm. As a test system, we selected a 21-mer antimicrobial peptide PGLa binding to an anionic DMPC/DMPG lipid bilayer. Application of REHT revealed the following binding mechanism. Due to the strong hydrophobic moment, the bound PGLa adopts an extensive helical structure. The binding free energy landscape identifies two major bound states, a metastable surface bound state and a dominant inserted state. In both states, positively charged PGLa amino acids maintain electrostatic interactions with anionic phosphate groups by rotating the PGLa helix around its axis. PGLa binding causes an influx of anionic DMPG and an efflux of zwitterionic DMPC lipids from the peptide proximity. PGLa thins the bilayer and disorders the adjacent fatty acid tails. Deep invasion of water wires into the bilayer hydrophobic core is detected in the inserted peptide state. The analysis of charge density distributions indicated that peptide positive charges are nearly compensated for by lipid negative charges and water dipole ordering, whereas ions play no role in peptide binding. Thus, electrostatic interactions are the key energetic factor in binding cationic PGLa to an anionic DMPC/DMPG bilayer. Comparison of REHT and REST shows that due to exclusion of lipids from tempered partition, REST lags behind REHT in peptide equilibration, particularly, with respect to peptide insertion and helix acquisition. As a result, REST struggles to provide accurate details of PGLa binding, although it still qualitatively maps the bimodal binding mechanism. Importantly, REHT not only equilibrates PGLa in the bilayer faster than REST, but also with less computational effort. We conclude that REHT is a preferable choice for studying interfacial biomolecular systems.

Translational protein RpsE as an alternative target for novel nucleoside analogues to treat MDR Enterobacter cloacae ATCC 13047: network analysis and molecular dynamics study

The pathogenic Enterobacter cloacae subsp. cloacae str. ATCC 13047 has contemporarily emerged as a multi-drug resistant strain. To formulate an effective treatment option, alternative therapeutic methods need to be explored. The present study focused on Gene Interaction Network study of 46 antimicrobial resistance genes to reveal the densely interconnecting and functional hub genes in E. cloacae ATCC 13047. The AMR genes were subjected to clustering, topological and functional enrichment analysis, revealing rpsE (RpsE), acrA (AcrA) and arnT (ArnT) as novel therapeutic drug targets for hindering drug resistance in the pathogenic strain. Network topology further indicated translational protein RpsE to be exploited as a promising drug-target candidate for which the structure was predicted, optimized and validated through molecular dynamics simulations (MDS). Absorption, distribution, metabolism and excretion screening recognized ZINC5441082 (N-Isopentyladenosine) (Lead_1) and ZINC1319816 (cyclopentyl-aminopurinyl-hydroxymethyl-oxolanediol) (Lead_2) as orally bioavailable compounds against RpsE. Molecular docking and MDS confirmed the binding efficacy and protein-ligand complex stability. Furthermore, binding free energy (Gbind) calculations, principal component and free energy landscape analyses affirmed the predicted nucleoside analogues against RpsE protein to be comprehensively examined as a potential treatment strategy against E. cloacae ATCC 13047.

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74.

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO2 uptake and CO2/N2 selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO2 adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO2 in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO2 in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

Binding free energy landscape of c-Src kinase to its inhibitors sampled by molecular dynamics simulations: Effect of ligand size and flexibility.

Protein kinases (PKs) play important roles in cellular signal transduction. Their dysfunction leads to disease such as cancer thus they are considered attractive drug targets. To design a selective PK inhibitor, we must elucidate the regulatory mechanism at the atomic level, which is difficult to obtain solely by experiments. We performed extensive molecular dynamics (MD) simulations to sample the binding free energy landscapes of c-Src kinase to its inhibitors of varying sizes and flexibilities. We used replica exchange MD simulations in two-dimensions to significantly enhance the sampling of the protein-ligand conformational space. We collected over 600 microseconds of trajectory data from which we obtained multiple un/binding events, obtained the structures of bound, intermediate and encounter poses and characterized the binding pathways. In addition, we investigated the effect of ligand size and flexibility on the binding mechanism and discuss possible implications on drug design.

Interdomain dynamics in human Replication Protein A regulates kinetics and thermodynamics of its binding to ssDNA.

Human Replication Protein A (hRPA) is a multidomain protein that interacts with ssDNA intermediates to provide the latter much-needed stability during DNA metabolism and maintain genomic integrity. Although the ssDNA organization with hRPA was studied recently through experimental means, characterizing the underlying mechanism at the atomic level remains challenging because of the dynamic domain architecture of hRPA and poorly understood heterogeneity of ssDNA-protein interactions. Here, we used a computational framework, precisely tailored to capture protein-ssDNA interactions, and investigated the binding of hRPA with a 60 nt ssDNA. Two distinct binding mechanisms are realized based on the hRPA domain flexibility. For a rigid domain architecture of hRPA, ssDNA binds sequentially with hRPA domains, resulting in slow association kinetics. The binding pathway involves the formation of stable and distinct intermediate states. On contrary, for a flexible domain architecture of hRPA, ssDNA binds synergistically to the A and B domains followed by the rest of hRPA. The domain dynamics in hRPA alleviates the free energy cost of domain orientation necessary for specific binding with ssDNA, leading to fast association kinetics along a downhill binding free energy landscape. An ensemble of free energetically degenerate intermediate states is encountered that makes it arduous to characterize them structurally. An excellent match between our results with the available experimental observations provides new insights into the rich dynamics of hRPA binding to ssDNA and in general paves the way to investigate intricate details of ssDNA-protein interactions, crucial for cellular functioning.

Binding free-energy landscapes of small molecule binder and non-binder to FMN riboswitch: All-atom molecular dynamics.

A small and flexible molecule, ribocil A (non-binder) or B (binder), binds to the deep pocket of the aptamer domain of the FMN riboswitch, which is an RNA molecule. This binding was studied by mD-VcMD, which is a generalized-ensemble simulation method. Ribocil A and B are structurally similar because they are optical isomers to each other. In the initial conformation of simulation, the ligands and the aptamer were completely dissociated in explicit solvent. The aptamer-ribocil B binding was stronger than the aptamer-ribocil A binding, which agrees with experiments. The computed free-energy landscape for the aptamer-ribocil B binding was funnel-like, whereas that for the aptamer-ribocil A binding was rugged. When passing through the gate (named "front gate") of the binding pocket, each ligand interacted with bases of the riboswitch by non-native π-π stackings, and the stackings restrained the ligand's orientation to be advantageous to reach the binding site smoothly. When the ligands reached the binding site in the pocket, the non-native stackings were replaced by the native stackings. The ligand's orientation restriction is discussed referring to a selection mechanism reported in an earlier work on a drug-GPCR interaction. The present simulation showed another pathway leading the ligands to the binding site. The gate ("rear gate") for this pathway was located completely opposite to the front gate on the aptamer's surface. However, the approach from the rear gate required overcoming a free-energy barrier regarding ligand's rotation before reaching the binding site.

"Dynamical Docking" of Cyclic Dinuclear Au(I) Bis-N-heterocyclic Complexes Facilitates Their Binding to G-Quadruplexes.

With the aim to improve the design of metal complexes as stabilizers of noncanonical DNA secondary structures, namely, G-quadruplexes (G4s), a series of cyclic dinuclear Au(I) N-heterocyclic carbene complexes based on xanthine and benzimidazole ligands has been synthesized and characterized by various methods, including X-ray diffraction. Fluorescence resonance energy transfer (FRET) and CD DNA melting assays unraveled the compounds' stabilization properties toward G4s of different topologies of physiological relevance. Initial structure-activity relationships have been identified and recognize the family of xanthine derivatives as those more selective toward G4s versus duplex DNA. The binding modes and free-energy landscape of the most active xanthine derivative (featuring a propyl linker) with the promoter sequence cKIT1 have been studied by metadynamics. The atomistic simulations evidenced that the Au(I) compound interacts noncovalently with the top G4 tetrad. The theoretical results on the Au(I) complex/DNA Gibbs free energy of binding were experimentally validated by FRET DNA melting assays. The compounds have also been tested for their antiproliferative properties in human cancer cells in vitro, showing generally moderate activity. This study provides further insights into the biological activity of Au(I) organometallics acting via noncovalent interactions and underlines their promise for tunable targeted applications by appropriate chemical modifications.

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses.

Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.

Computationally Elucidating the Binding Kinetics for Different AChE Inhibitors to Access the Rationale for Improving the Drug Efficacy.

Traditional drug discovery is based on a binding affinity (thermodynamics)-driven paradigm. Numerous examples, however, demonstrated that drug efficacy does not always depend only on binding affinity but positively correlates with binding kinetics, that is, the dissociation rate constant (koff). Binding free energy landscape (BFEL) constructor is a computational binding kinetics prediction method, previously developed by us, that estimates the binding kinetics for ligand-protein based on their constructed binding free energy landscape, but it also reveals the detailed molecular mechanism of the binding event, hence, providing the position of transition states at the molecular level to modify/improve the binding kinetics. Acetylcholinesterase (AChE) is a well-known Alzheimer's disease (AD) target for which there is still not an ideal drug on the market. Therefore, to improve the drug design strategy for AD, the binding kinetics and binding molecular mechanisms of the four inhibitors of AChE, that is, E2020 (Aricept), HupA, Rivastigmine, and Galantamine, were studied. Also, the differentiation of the binding kinetics between mAChE and TcAChE was studied to evaluate the sensitiveness of BFEL constructor. The flexibility of molecules has a noticeable effect on the nature of BFEL. To the same target, flexible molecules (i.e., E2020 and Rivastigmine) which contain more rotatable bonds tend to have more complicated BFELs reflecting more complicated molecular action mechanisms than the rigid ones (i.e., HupA and Galantamine), which therefore could be more challenging to be optimized. The binding kinetics is highly dependent on the structure of the molecules, such as the length and the functional groups. Therefore, E2020 presents better binding kinetic and thermodynamic properties with either TcAChE or mAChE. Therefore, it is the most promising lead drug for binding kinetics-based drug design. In addition, the binding kinetics of a drug may present different values in the proteins of different organisms because the residue compositions of the binding gorges of the targets are variant, that is, E2020 shows lower binding affinity and association energy barrier in binding with mAChE than TcAChE. However, HupA presents a better binding property with TcAChE than mAChE.

Ion binding free-energy landscapes for telomeric and terra G-quadruplexes

G-quadruplexes (GQs) are noncanonical structures formed by nucleic acid sequences that are rich in guanines, which form tetrads via Hoogsteen hydrogen bonding networks. GQs are commonly found in promoters, telomeres, and untranslated regions in mRNA, suggesting roles in gene expression and genomic integrity and making GQs promising targets for drug design. Previous work studied the telomeric DNA GQ and the analogous telomeric repeat-containing RNA (TERRA) GQ using molecular dynamics simulations with the Drude-2017 polarizable force field.

Probing Allosteric Modulation of Membrane Receptor in the Native State by Data Mining-Integrated Tracking Microscopy

Probing Allosteric Modulation of Membrane Receptor in the Native State by Data Mining-Integrated Tracking Microscopy

Adsorption of CO2 and CH4 in MIL-47 investigated by the 3D-RISM molecular theory of solvation.

Metal-organic frameworks (MOFs) comprise a class of highly porous nanomaterials formed by the assembly of organic molecular templates connected by metal ions. These materials exhibit a large diversity of pore size and geometry, topology, surface area, and chemical functionality. MOFs are particularly promising materials for developing new technologies for capture and storage of greenhouse gases such as methane and carbon dioxide. Here we apply the three dimensional reference interaction site model (3D-RISM) molecular theory of solvation to study the interactions of CO2 and CH4 with the metal-organic material MIL-47. The 3D-RISM integral equations were solved to determine the three dimensional density correlation functions of the gas (solvent) relative to the atomic positions of the MIL-47 framework, treated as static solute sites. The computed solvent spatial distributions inside MIL-47 pores were used to identify whether or not there exist preferable binding sites and the binding free energy landscape for the gas of interest at low computational costs compared with other molecular modeling techniques, such as grand canonical Monte Carlo and molecular dynamics simulations. The 3D-RISM formalism was applied to pure CO2, pure CH4, and binary mixtures of these gases of various compositions under different pressure conditions. The results indicate that both gases bind very weakly to MIL-47 and that this material exhibits nearly vanishing CO2/CH4 selectivity. The 3D-RISM computations presented here can be extended to investigate the physical adsorption of gases on other MOFs and nanoporous materials, providing an alternative low-cost computational approach to study gas capture and storage in nanoporous materials in general and, in particular, to determine the binding free-energy landscape in these systems.

High-Resolution Imaging of Maltoporin LamB while Quantifying the Free-Energy Landscape and Asymmetry of Sugar Binding.

Maltoporins are a family of membrane proteins that facilitate the diffusion of hydrophilic molecules and maltosaccharides across the outer membrane of Gram-negative bacteria. Two contradicting models propose the sugar binding, uptake, and transport by maltoporins to be either symmetric or asymmetric. Here, we address this contradiction and introduce force-distance-based atomic force microscopy to image single maltoporin LamB trimers in the membrane at sub-nanometer resolution and simultaneously quantify the binding of different malto-oligosaccharides. We assay subtle differences of the binding free-energy landscape of maltotriose, maltotetraose, and maltopentaose, which quantifies how binding strength and affinity increase with the malto-oligosaccharide chain length. The ligand-binding parameters change considerably by mutating the extracellular loop 3, which folds into and constricts the transmembrane pore of LamB. By recording LamB topographs and structurally mapping binding events at sub-nanometer resolution, we observe LamB to preferentially bind maltodextrin from the periplasmic side, which shows sugar binding and uptake to be asymmetric. The study introduces atomic force microscopy as an analytical nanoscopic tool that can differentiate among the factors modulating and models describing the binding and uptake of substrates by membrane proteins.

On the Mechanism of Gold/NHC Compounds Binding to DNA G-Quadruplexes: Combined Metadynamics and Biophysical Methods.

The binding modes and free-energy landscape of two AuI /N-heterocyclic carbene complexes interacting with G-quadruplexes, namely a human telomeric (hTelo) and a promoter sequence (C-KIT1), are studied here for the first time by metadynamics. The theoretical results are validated by FRET DNA melting assays and provide an accurate estimate of the absolute gold complex/DNA binding free energy. This advanced in silico approach is valuable to achieve rational drug design of selective G4 binders.

Expression of Concern for "Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes".

Expression of Concern for "Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes".

Intrinsic Conformational Preferences and Interactions in α-Synuclein Fibrils: Insights from Molecular Dynamics Simulations.

Amyloid formation by the intrinsically disordered α-synuclein protein is the hallmark of Parkinson's disease. We present atomistic Molecular Dynamics simulations of the core of α-synuclein using enhanced sampling techniques to describe the conformational and binding free energy landscapes of fragments implicated in fibril stabilization. The theoretical framework is derived to combine the free energy profiles of the fragments into the reaction free energy of a protein binding to a fibril. Our study shows that individual fragments in solution have a propensity toward attaining non-β conformations, indicating that in a fibril β-strands are stabilized by interactions with other strands. We show that most dimers of hydrogen-bonded fragments are unstable in solution, while hydrogen bonding stabilizes the collective binding of five fragments to the end of a fibril. Hydrophobic effects make further contributions to the stability of fibrils. This study is the first of its kind where structural and binding preferences of the five major fragments of the hydrophobic core of α-synuclein have been investigated. This approach improves sampling of intrinsically disordered proteins, provides information on the binding mechanism between the core sequences of α-synuclein, and enables the parametrization of coarse grained models.

The Molecular Mechanism Underlying Ligand Binding to the Membrane-Embedded Site of a G-Protein-Coupled Receptor.

The crystal structure of P2Y1 receptor (P2Y1R), a class A GPCR, revealed a special extra-helical site for its antagonist, BPTU, which locates in-between the membrane and the protein. However, due to the limitation of crystallization experiments, the membrane was mimicked by use of detergents, and the information related to the binding of BPTU to the receptor in the membrane environment is rather limited. In the present work, we conducted a total of ∼7.5 μs all-atom simulations in explicit solvent using conventional molecular dynamics and multiple enhanced sampling methods, with models of BPTU and a POPC bilayer, both in the absence and presence of P2Y1R. Our simulations revealed that BPTU prefers partitioning into the interface of polar/lipophilic region of the lipid bilayer before associating with the receptor. Then, it interacts with the second extracellular loop of the receptor and reaches the binding site through the lipid-receptor interface. In addition, by use of funnel-metadynamics simulations which efficiently enhance the sampling of bound and unbound states, we provide a statistically accurate description of the underlying binding free energy landscape. The calculated absolute ligand-receptor binding affinity is in excellent agreement with the experimental data (Δ Gb0_theo = -11.5 kcal mol-1, Δ Gb0_exp= -11.7 kcal mol-1). Our study broadens the view of the current experimental/theoretical models and our understanding of the protein-ligand recognition mechanism in the lipid environment. The strategy used in this work is potentially applicable to investigate ligands association/dissociation with other membrane-embedded sites, allowing identification of compounds targeting membrane receptors of pharmacological interest.