Prediction of AChE-ligand affinity using the umbrella sampling simulation

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

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.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (k_off) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the k_off value. We observed a high correlation among the simulated, predicted, and experimental -log(k_off) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our k_off predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental k_off values and are not in our ML training dataset. The predicted k_off values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict k_off of other proteins and will enhance the kinetics-based drug design endeavor.

Design, Synthesis, and Characterization of a Novel 2'-5'-Linked Amikacin-Binding Aptamer: An Experimental and MD Simulation Study

To extend the approach of using RNA aptamers as transient protective groups for the synthesis of novel small-molecule drug derivatives from the existing aminoglycosides, we incorporated 2'-5' phosphodiester backbone modification in a structurally known neomycin RNA aptamer and studied the binding of a series of aminoglycosides using isothermal calorimetry (ITC) and molecular dynamics (MD) simulation. Experimental characterization of amikacin, a commercially available and widely used aminoglycoside for treating bacterial infections, shows that the aptamer A1 with a 2'-5' linkage between G15 and U16 exhibits a sevenfold increase in binding affinity with a lower binding energy compared to the native aptamer. Molecular dynamics (MD) simulation studies rationalize that this noncanonical linkage generates a narrower binding pocket by creating a superspiral RNA helical structure, which improves the ligand's fit in the binding pocket. These results provide new insights into applying 2'-5' linkages to diversify functional RNA aptamers as noncovalent protective groups in the synthesis of aminoglycoside derivatives, which can be further extended to other current drug molecules and complex natural compounds to make new pools of drug candidates more efficiently.

Probing interactions of aminoacyl-adenylate with Mycobacterium tuberculosis methionyl-tRNA synthetase through in silico site-directed mutagenesis and free energy calculation

Methionyl-tRNA synthetase (MetRS) is an attractive molecular target for antibiotic discovery. Recently, we have developed several classes of small-molecular inhibitors of Mycobacterium tuberculosis MetRS possessing antibacterial activity. In this article, we performed in silico site-directed mutagenesis of aminoacyl-adenylate binding site of M. tuberculosis MetRS in order to identify crucial amino acid residues for substrate interaction. The umbrella sampling algorithm was used to calculate the binding free energy (ΔG) of these mutated forms with methionyl-adenylate analogue. According to the obtained results, the replacement of Glu24 and Leu293 by alanine leads to the most significant decrease in the binding free energy (ΔG) for adenylate analogue with methionyl-tRNA synthetase indicating increasing of the affinity, which in turn causes the loss of compounds inhibitory activity. Therefore, these amino acid residues can be proposed for further experimental site-directed mutagenesis to confirm binding mode of inhibitors and should be taken into account during chemical optimization to overcome resistance due to mutations.Communicated by Ramaswamy H. Sarma.

Probing the Molecular Basis of Cofactor Affinity and Conformational Dynamics of Mycobacterium tuberculosis Elongation Factor Tu: An Integrated Approach Employing Steered Molecular Dynamics and Umbrella Sampling Simulations

The emergence of multidrug-resistant and extensively drug-resistant tuberculosis strains is the reason that the infectious tuberculosis pathogen is still the most common cause of death. The quest for new antitubercular drugs that can fit into multidrug regimens, function swiftly, and overcome the ever-increasing prevalence of drug resistance continues. The crucial role of MtbEF-Tu in translation and trans-translation processes makes it an excellent target for antitubercular drug design. In this study, the primary sequence of MtbEF-Tu was used to model the three-dimensional structures of MtbEF-Tu in the presence of GDP ("off" state) and GTP ("on" state). The binding free energy computed using both the molecular mechanics/Poisson-Boltzmann surface area and umbrella sampling approaches shows that GDP binds to MtbEF-Tu with an ∼2-fold affinity compared to GTP. The steered molecular dynamics (SMD) and umbrella sampling simulation also shows that the dissociation of GDP from MtbEF-Tu in the presence of Mg²⁺ is a thermodynamically intensive process, while in the absence of Mg²⁺, the destabilized GDP dissociates very easily from the MtbEF-Tu. Naturally, the dissociation of Mg²⁺ from the MtbEF-Tu is facilitated by the nucleotide exchange factor EF-Ts, and this prior release of magnesium makes the dissociation process of destabilized GDP easy, similar to that observed in the umbrella sampling and SMD study. The MD simulations of MtbEF-Tu's "on" state conformation in the presence of GTP reveal that the secondary structure of switch-I and Mg²⁺ coordination network remains similar to its template despite the absence of identity in the conserved region of switch-I. On the other hand, the secondary structure in the conserved region of the switch-I of MtbEF-Tu unwinds from a helix to a loop in the presence of GDP. The major conformational changes observed in switch-I and the movement of Thr64 away from Mg²⁺ mainly reflect essential conformational changes to make the shift of MtbEF-Tu's "on" state to the "off" state in the presence of GDP. These obtained structural and functional insights into MtbEF-Tu are pivotal for a better understanding of structural-functional linkages of MtbEF-Tu, and these findings may serve as a basis for the design and development of MtbEF-Tu-specific inhibitors.

The Molecular Mechanism of Human Voltage-Dependent Anion Channel 1 Blockade by the Metallofullerenol Gd@C82(OH)22: An In Silico Study

The endohedral metallofullerenol Gd@C₈₂(OH)₂₂ has been identified as a possible antineoplastic agent that can inhibit both the growth and metastasis of cancer cells. Despite these potentially important effects, our understanding of the interactions between Gd@C₈₂(OH)₂₂ and biomacromolecules remains incomplete. Here, we study the interaction between Gd@C₈₂(OH)₂₂ and the human voltage-dependent anion channel 1 (hVDAC1), the most abundant porin embedded in the mitochondrial outer membrane (MOM), and a potential druggable target for novel anticancer therapeutics. Using in silico approaches, we observe that Gd@C₈₂(OH)₂₂ molecules can permeate and form stable interactions with the pore of hVDAC1. Further, this penetration can occur from either side of the MOM to elicit blockage of the pore. The binding between Gd@C₈₂(OH)₂₂ and hVDAC1 is largely driven by long-range electrostatic interactions. Analysis of the binding free energies indicates that it is thermodynamically more favorable for Gd@C₈₂(OH)₂₂ to bind to the hVDAC1 pore when it enters the channel from inside the membrane rather than from the cytoplasmic side of the protein. Multiple factors contribute to the preferential penetration, including the surface electrostatic landscape of hVDAC1 and the unique physicochemical properties of Gd@C₈₂(OH)₂₂. Our findings provide insights into the potential molecular interactions of macromolecular biological systems with the Gd@C₈₂(OH)₂₂ nanodrug.

Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations

The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.

Searching and designing potential inhibitors for SARS-CoV-2 Mpro from natural sources using atomistic and deep-learning calculations

The spread of severe acute respiratory syndrome coronavirus 2 novel coronavirus (SARS-CoV-2) worldwide has caused the coronavirus disease 2019 (COVID-19) pandemic. A hundred million people were infected, resulting in several millions of death worldwide. In order to prevent viral replication, scientists have been aiming to prevent the biological activity of the SARS-CoV-2 main protease (3CL pro or Mpro). In this work, we demonstrate that using a reasonable combination of deep-learning calculations and atomistic simulations could lead to a new approach for developing SARS-CoV-2 main protease (Mpro) inhibitors. Initially, the binding affinities of the natural compounds to SARS-CoV-2 Mpro were estimated via atomistic simulations. The compound tomatine, thevetine, and tribuloside could bind to SARS-CoV-2 Mpro with nanomolar/high-nanomolar affinities. Secondly, the deep-learning (DL) calculations were performed to chemically alter the top-lead natural compounds to improve ligand-binding affinity. The obtained results were then validated by free energy calculations using atomistic simulations. The outcome of the research will probably boost COVID-19 therapy.

The hybrid DeepFrag/atomistic simulation approach could lead to a new scheme for developing SARS-CoV-2 3CLpro/Mpro inhibitors.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters

AutoDock Vina (Vina) achieved a very high docking-success rate, p ^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R set 1 = 0.556 ± 0.025 compared with R Default = 0.493 ± 0.028 obtained by the original Vina and R Vina 1.2 = 0.503 ± 0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R ≥ 0.500 for 32/48 targets, compared with the default package, giving R ≥ 0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( R set 1 = 0.617 ± 0.017 ) than the default package ( R Default = 0.543 ± 0.020 ) and Vina version 1.2 ( R Vina 1.2 = 0.540 ± 0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

Insight into the impact of EGFR L792Y/F/H mutations on sensitivity to osimertinib: an in silico study

EGFR L792Y/F/H mutation makes it difficult for Osimertinib to recognize ATP pockets.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Estimating the ligand-binding affinity via λ-dependent umbrella sampling simulations

The umbrella sampling (US) approach has been demonstrated to be a very efficient method for estimating the ligand-binding affinity. However, most of the calculated values overestimate experimental ones that are probably caused by the inaccurate representation of the interaction between the ligand and the surrounding molecules. The issue can be resolved via the implementation aspects of λ-alteration simulation into the US approach, which we call the λ-dependent umbrella sampling (λUS) scheme. In particular, the electrostatic and van der Waals interactions were simultaneously changed by using the coupling parameter λ during λUS simulations. The mean value of obtained results, ∆ G US λ = 0.20 = - 11.59 ± 1.51 kcal mol^-1 , is in good fitting to the mean value of respective experiments, ∆G_EXP =  - 11.26 ± 0.89 kcal mol^-1 . Moreover, the correlation between the proposed approach and experiment is quite good with a value of R US λ = 0.20 = 0.82 ± 0.10 . The λUS scheme significantly enhances the calculated accuracy since the RMSE of the proposed scheme is smaller than traditional US simulations, RMSE US λ = 0.20 = 2.99 ± 0.82 kcal mol^-1 versus RMSE US λ = 0.00 = 5.48 ± 0.81 kcal mol^-1 . Furthermore, the precision is increased since the computed error via λUS approach, δ US λ = 0.20 = 1.51 kcal mol^-1 , was smaller than those of the US simulation, δ US λ = 0.00 = 1.78 kcal mol^-1 . Overall, the proposed approach perhaps provides an efficient way to accurately and precisely estimate the ligand-binding free energy.

Estimation of the ligand-binding free energy of checkpoint kinase 1 via non-equilibrium MD simulations

Checkpoint kinase 1 (CHK1) is a serine/threonine-protein kinase that is involved in cell cycle regulation in eukaryotes. Inhibition of CHK1 is thus considered as a promising approach in cancer therapy. In this study, the fast pulling of ligand (FPL) process was applied to predict the relative binding affinities of CHK1 inhibitors using non-equilibrium molecular dynamics (MD) simulations. The work of external harmonic forces to pull the ligand out of the binding cavity strongly correlated with the experimental binding affinity of CHK1 inhibitors with the correlation coefficient of R = -0.88 and an overall root mean square error (RMSE) of 0.99 kcal/mol. The data indicate that the FPL method is highly accurate in predicting the relative binding free energies of CHK1 inhibitors with an affordable CPU time. A new set of molecules were designed based on the molecular modeling of interactions between the known inhibitor and CHK1 as inhibitory candidates. Molecular docking and FPL results exhibited that the binding affinities of developed ligands were similar to the known inhibitor in interaction with the catalytic site of CHK1, producing very potential CHK1 inhibitors of that the inhibitory activities should be further evaluated in vitro.

An activity prediction model for steroidal and triterpenoidal inhibitors of Acetylcholinesterase enzyme

Nowadays, the importance of computational methods in the design of therapeutic agents in a more efficient way is indisputable. Particularly, these methods have been important in the design of novel acetylcholinesterase enzyme inhibitors related to Alzheimer's disease. In this sense, in this report a computational model of linear prediction of acetylcholinesterase inhibitory activity of steroids and triterpenes is presented. The model is based in a correlation between binding energies obtained from molecular dynamic simulations (after docking studies) and [Formula: see text] values of a training set. This set includes a family of natural and semi-synthetic structurally related alkaloids reported in bibliography. These types of compounds, with some structural complexity, could be used as building blocks for the synthesis of many important biologically active compounds Therefore, the present study proposes an alternative based on the use of conventional and easily accessible tools to make progress on the rational design of molecules with biological activity.

Donepezil Inhibits Acetylcholinesterase via Multiple Binding Modes at Room Temperature

Donepezil is a second generation acetylcholinesterase (AChE) inhibitor for treatment of Alzheimer's disease (AD). AChE is important for neurotransmission at neuromuscular junctions and cholinergic brain synapses by hydrolyzing acetylcholine into acetate and choline. In vitro data support that donepezil is a reversible, mixed competitive and noncompetitive inhibitor of AChE. The experimental fact then suggests a more complex binding mechanism beyond the molecular view in X-ray models resolved at cryogenic temperatures that show a unique binding mode of donepezil in the active site of the enzyme. Aiming at clarifying the mechanism behind that mixed competitive and noncompetitive nature of the inhibitor, we have applied molecular dynamics (MD) simulations and docking and free-energy calculations to investigate microscopic details and energetics of donepezil association for conditions of substrate-free and -bound states of the enzyme. Liquid-phase MD simulation at room temperature shows AChE transits between "open" and "closed" conformations to control accessibility to the active site and ligand binding. As shown by docking and free-energy calculations, association of donepezil involves its reversible axial displacement and reorientation in the active site of the enzyme, assisted by water molecules. Donepezil binds equally well the main-door anionic binding site PAS, the acyl pocket, and the catalytic site CAS by respectively adopting outward-inward-inward orientations regardless of substrate occupancy-the overall stability of that reaction process depends however on co-occupancy of the enzyme being preferential for its substrate-free state. All together, our findings support a physiologically relevant mechanism of AChE inhibition by donepezil involving multistable interactions modes at the molecular origin of the inhibitor's activity.

Effective estimation of the inhibitor affinity of HIV-1 protease via a modified LIE approach

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses. Computer-aided drug design (CADD) would enhance the discovery of new therapies, through which the estimation of ligand-binding affinity is critical to predict the most efficient inhibitor. A time-consuming binding free energy method would reduce the usefulness of CADD. The modified linear interaction energy (LIE) approach emerges as an appropriate protocol that performs this task. In particular, the polar interaction free energy, which is obtained via numerically resolving the linear Poisson–Boltzmann equation, plays as an important role in driving the binding mechanism of the HIV-1 PR + inhibitor complex. The electrostatic interaction energy contributes to the attraction between two molecules, but the vdW interaction acts as a repulsive factor between the ligand and the HIV-1 PR. Moreover, the ligands were found to adopt a very strong hydrophobic interaction with the HIV-1 PR. Furthermore, the results obtained corroborate the high accuracy and precision of computational studies with a large correlation coefficient value R = 0.83 and a small RMSE δ_RMSE = 1.25 kcal mol⁻¹. This method is less time-consuming than the other end-point methods, such as the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) and free energy perturbation (FEP) approaches. Overall, the modified LIE approach would provide ligand-binding affinity with HIV-1 PR accurately, precisely, and rapidly, resulting in a more efficient design of new inhibitors.

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses.