A Self-Adaptive Steered Molecular Dynamics Method Based on Minimization of Stretching Force Reveals the Binding Affinity of Protein-Ligand Complexes

Hinokiflavone Attenuates the Virulence of Methicillin-Resistant Staphylococcus aureus by Targeting Caseinolytic Protease P

Drug-resistant bacteria was the third leading cause of death worldwide in 2019, which sounds like a cautionary note for global public health. Therefore, developing novel strategies to combat Methicillin-resistant Staphylococcus aureus (MRSA) infections is the need of the hour. Caseinolytic protease P (ClpP) represents pivotal microbial degradation machinery in MRSA involved in bacterial homeostasis and pathogenicity, considered an ideal target for combating S. aureus infections. Herein, we identified a natural compound, hinokiflavone, that inhibited the activity of ClpP of MRSA strain USA300 with an IC₅₀ of 34.36 μg/mL. Further assays showed that hinokiflavone reduced the virulence of S. aureus by inhibiting multiple virulence factors expression. Results obtained from cellular thermal transfer assay (CETSA), thermal shift assay (TSA), local surface plasmon resonance (LSPR) and molecular docking (MD) assay enunciated that hinokiflavone directly bonded to ClpP with confirmed docking sites, including SER-22, LYS-26 and ARG-28. In vivo, the evaluation of anti-infective activity showed that hinokiflavone in combination with vancomycin effectively protected mice from MRSA-induced fatal pneumonia, which was more potent than vancomycin alone. As mentioned above, hinokiflavone, as an inhibitor of ClpP, could be further developed into a promising adjuvant against S. aureus infections.

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

Steered molecular dynamics (SMD) simulation is a powerful method in computer-aided drug design as it can be used to access the relative binding affinity with high precision but with low computational cost. The success of SMD depends on the choice of the direction along which the ligand is pulled from the receptor-binding site. In most simulations, the unidirectional pathway was used, but in some cases, this choice resulted in the ligand colliding with the complex surface of the exit tunnel. To overcome this difficulty, several variants of SMD with multidirectional pulling have been proposed, but they are not completely devoid of disadvantages. Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. The effectiveness of our protocol was demonstrated by finding expulsion pathways of Huperzine A and camphor from the binding site of Torpedo California acetylcholinesterase and P450cam proteins, respectively, and comparing them with the previous results obtained using memetic sampling and random acceleration molecular dynamics. In addition, by applying this protocol to a set of ligands bound with LSD1 (lysine specific demethylase 1), we obtained a much higher correlation between the work of pulling force and experimental data on the inhibition constant IC50 compared to that obtained using the unidirectional approach based on minimal steric hindrance.

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.

Three New Scorpion Chloride Channel Toxins as Potential Anti-Cancer Drugs: Computational Prediction of The Interactions With Hmmp-2 by Docking and Steered Molecular Dynamics Simulations

Scorpion venom is a rich source of toxins which have great potential to develop new therapeutic agents. Scorpion chloride channel toxins (ClTxs), such as Chlorotoxin selectively inhibit human Matrix Methaloproteinase-2 (hMMP-2). The inhibitors of hMMP-2 have potential use in cancer therapy. Three new ClTxs, meuCl14, meuCl15 and meuCl16, derived from the venom transcriptome of Iranian scorpion, M. eupeus (Buthidea family), show high sequence identity (71.4%) with Chlorotoxin. Here, 3-D homology model of new ClTxs were constructed. The models were optimized by Molecular Dynamics simulation based on MDFF (molecular dynamics flexible fitting) method. New ClTxs indicate the presence of CSαβ folding of other scorpion toxins. A docking followed by steered molecular dynamics (SMD) simulations to investigate the interactions of meuCl14, meuCl15, and meuCl16 with hMMP-2 was applied. The current study creates a correlation between the unbinding force and the inhibition activities of meuCl14, meuCl15 and meuCl16 to shed some insights as to which toxin may be used as a drug deliverer. To this aim, SMD simulations using Constant Force Pulling method were carried out. The SMD provided useful details related to the changes of electrostatic, van de Waals (vdW), and hydrogen-bonding (H-bonding) interactions between ligands and receptor during the pathway of unbinding. According to SMD results, the interaction of hMMP-2 with meuCl14 is more stable. In addition, Arginine residue was found to contribute significantly in interaction of ClTxs with hMMP-2. All in all, the present study is a dynamical approach whose results are capable of being implemented in structure-based drug design.

Tuning the ring strain effect in acridine derivatives on binding affinity with G-quadruplex-DNA: A computational and experimental study

Search for inhibitors to stabilize the telomeric G-quadruplex in order to deter telomerase activity is an active area of research. Inhibitors play an important role to initiate the tumor cell mortalization process. This work reports for the first time of acridine derivative with four membered ammonium rings at the side chain to surpass the binding ability against BRACO-19 with G-quadruplex-DNA. It is known in the literature that acridine based molecule BRACO-19 can effectively bind with G-quadruplex-DNA. The computational study performed in this study revealed that the binding ability of acridine based molecule can be augmented with subtle variation in the molecular structure of the drug like candidates. Steered molecular dynamics (SMD) performed with the acridine derivatives and G-quadruplex DNA showed the importance of ring strain to the side chain of those ligand molecules. The rupture force analysis, hydrogen bonding interactions and the calculated free energies in MM-PBSA method suggest that ligand 3 is superior than that of BRACO-19. The synthesized ligand 3 and BRACO-19 showed the binding constants obtained from ITC measurements are 4 × 10⁶ mol^-1 and 2.6 × 10⁶, which corroborates the computational findings.

Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes

Protein-peptide interactions play essential roles in many cellular processes and their structural characterization is the major focus of current experimental and theoretical research. Two decades ago, it was proposed to employ the steered molecular dynamics (SMD) to assess the strength of protein-peptide interactions. The idea behind using SMD simulations is that the mechanical stability can be used as a promising and an efficient alternative to computationally highly demanding estimation of binding affinity. However, mechanical stability defined as a peak in force-extension profile depends on the choice of the pulling direction. Here we propose an uncommon choice of the pulling direction along resultant dipole moment (RDM) vector, which has not been explored in SMD simulations so far. Using explicit solvent all-atom MD simulations, we apply SMD technique to probe mechanical resistance of ligand-receptor system pulled along two different vectors. A novel pulling direction—when ligand unbinds along the RDM vector—results in stronger forces compared to commonly used ligand unbinding along center of masses vector. Our observation that RDM is one of the factors influencing the mechanical stability of protein-peptide complex can be used to improve the ranking of binding affinities by using mechanical stability as an effective scoring function.

Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Binding affinity of a small ligand to a receptor is the important quantity in drug design, and it might be characterized by different quantities. The most popular one is the binding free energy, which can be estimated by several methods in conventional molecular dynamics simulation. So far in steered molecular dynamics (SMD), one can use either the rupture force or nonequilibrium pulling work as a measure for binding affinity. In this paper, we have shown that the nonequilibrium binding free energy Δ G_neq^Jar, obtained by Jarzynski's equality at a finite pulling speed, has good correlation with experimental data on inhibition constants, implying that this quantity can be used as a good scoring function for binding affinity. A similar correlation has also been disclosed for binding and unbinding free energy barriers. Applying the SMD method to unbinding of 23 small compounds from the binding site of β-lactamase protein, a bacteria-produced enzyme, we have demonstrated that the rupture or unbinding time strongly correlates with experimental data with correlation level R ≈ 0.84. As follows from the Jarzynski's equality, the rupture time depends on the unbinding barrier exponentially. We show that Δ G_neq^Jar, the rupture time, and binding and unbinding free energy barriers are good descriptors for binding affinity. Our observation may be useful for fast screening of potential leads as the SMD simulation is not time-consuming. On the basis of nonequilibrium simulation, we disclosed that, in agreement with the experiment, the binding time is much longer than the unbinding one.

Protocol for fast screening of multi-target drug candidates: Application to Alzheimer's disease

The treatment of many diseases may require drugs that are capable to attack multiple targets simultaneously. Obviously, the virtual screening of multi-target drug candidates is much more time consuming compared to the single-target case. This, in particular, concerns the last step of virtual screening where the binding free energy is computed by conventional molecular dynamics simulation. To overcome this difficulty we propose a simple protocol which is relied on the fast steered molecular dynamics simulation and on available experimental data on binding affinity of reference ligand to a given target. Namely, first we compute non-equilibrium works generated during pulling ligands from the binding site using the steered molecular dynamics method. Then as top leads we choose only those compounds that have the non-equilibrium work larger than that of a reference compound for which the binding free energy has been already known from experiment. Despite many efforts no cures for AD (Alzheimer's disease) have been found. One of possible reasons for this failure is that drug candidates were developed for a single target, while there are exist many possible pathways to AD. Applying our new protocol to five targets including amyloid beta fibril, peroxisome proliferator-activated receptor γ, retinoic X receptor α, β- and γ-secretases, we have found two potential drugs (CID 16040294 and CID 9998128) for AD from the large PubChem database. We have also shown that these two ligands can interfere with the activity of popular Acetylcholinesterase target through strong binding towards it.

Ligand diffusion in proteins via enhanced sampling in molecular dynamics

Computational simulations in biophysics describe the dynamics and functions of biological macromolecules at the atomic level. Among motions particularly important for life are the transport processes in heterogeneous media. The process of ligand diffusion inside proteins is an example of a complex rare event that can be modeled using molecular dynamics simulations. The study of physical interactions between a ligand and its biological target is of paramount importance for the design of novel drugs and enzymes. Unfortunately, the process of ligand diffusion is difficult to study experimentally. The need for identifying the ligand egress pathways and understanding how ligands migrate through protein tunnels has spurred the development of several methodological approaches to this problem. The complex topology of protein channels and the transient nature of the ligand passage pose difficulties in the modeling of the ligand entry/escape pathways by canonical molecular dynamics simulations. In this review, we report a methodology involving a reconstruction of the ligand diffusion reaction coordinates and the free-energy profiles along these reaction coordinates using enhanced sampling of conformational space. We illustrate the above methods on several ligand-protein systems, including cytochromes and G-protein-coupled receptors. The methods are general and may be adopted to other transport processes in living matter.

Computing the binding affinity of a ligand buried deep inside a protein with the hybrid steered molecular dynamics

Computing the ligand-protein binding affinity (or the Gibbs free energy) with chemical accuracy has long been a challenge for which many methods/approaches have been developed and refined with various successful applications. False positives and, even more harmful, false negatives have been and still are a common occurrence in practical applications. Inevitable in all approaches are the errors in the force field parameters we obtain from quantum mechanical computation and/or empirical fittings for the intra- and inter-molecular interactions. These errors propagate to the final results of the computed binding affinities even if we were able to perfectly implement the statistical mechanics of all the processes relevant to a given problem. And they are actually amplified to various degrees even in the mature, sophisticated computational approaches. In particular, the free energy perturbation (alchemical) approaches amplify the errors in the force field parameters because they rely on extracting the small differences between similarly large numbers. In this paper, we develop a hybrid steered molecular dynamics (hSMD) approach to the difficult binding problems of a ligand buried deep inside a protein. Sampling the transition along a physical (not alchemical) dissociation path of opening up the binding cavity---pulling out the ligand---closing back the cavity, we can avoid the problem of error amplifications by not relying on small differences between similar numbers. We tested this new form of hSMD on retinol inside cellular retinol-binding protein 1 and three cases of a ligand (a benzylacetate, a 2-nitrothiophene, and a benzene) inside a T4 lysozyme L99A/M102Q(H) double mutant. In all cases, we obtained binding free energies in close agreement with the experimentally measured values. This indicates that the force field parameters we employed are accurate and that hSMD (a brute force, unsophisticated approach) is free from the problem of error amplification suffered by many sophisticated approaches in the literature.

Unraveling the Conformational Landscape of Ligand Binding to Glucose/Galactose-Binding Protein by Paramagnetic NMR and MD Simulations

Protein dynamics related to function can nowadays be structurally well characterized (i.e., instances obtained by high resolution structures), but they are still ill-defined energetically, and the energy landscapes are only accessible computationally. This is the case for glucose-galactose binding protein (GGBP), where the crystal structures of the apo and holo states provide structural information for the domain rearrangement upon ligand binding, while the time scale and the energetic determinants for such concerted dynamics have been so far elusive. Here, we use GGBP as a paradigm to define a functional conformational landscape, both structurally and energetically, by using an innovative combination of paramagnetic NMR experiments and MD simulations. Anisotropic NMR parameters induced by self-alignment of paramagnetic metal ions was used to characterize the ensemble of conformations adopted by the protein in solution while the rate of interconversion between conformations was elucidated by long molecular dynamics simulation on two states of GGBP, the closed-liganded (holo_cl) and open-unloaded (apo_op) states. Our results demonstrate that, in its apo state, the protein coexists between open-like (68%) and closed-like (32%) conformations, with an exchange rate around 25 ns. Despite such conformational heterogeneity, the presence of the ligand is the ultimate driving force to unbalance the equilibrium toward the holo_cl form, in a mechanism largely governed by a conformational selection mechanism.