A New Method for Navigating Optimal Direction for Pulling Ligand from Binding Pocket: Application to Ranking Binding Affinity by Steered Molecular Dynamics

Binding of SARS-CoV-2 Nonstructural Protein 1 to 40S Ribosome Inhibits mRNA Translation

Experimental evidence has established that SARS-CoV-2 NSP1 acts as a factor that restricts cellular gene expression and impedes mRNA translation within the ribosome's 40S subunit. However, the precise molecular mechanisms underlying this phenomenon have remained elusive. To elucidate this issue, we employed a combination of all-atom steered molecular dynamics and coarse-grained alchemical simulations to explore the binding affinity of mRNA to the 40S ribosome, both in the presence and absence of SARS-CoV-2 NSP1. Our investigations revealed that the binding of SARS-CoV-2 NSP1 to the 40S ribosome leads to a significant enhancement in the binding affinity of mRNA. This observation, which aligns with experimental findings, strongly suggests that SARS-CoV-2 NSP1 has the capability to inhibit mRNA translation. Furthermore, we identified electrostatic interactions between mRNA and the 40S ribosome as the primary driving force behind mRNA translation. Notably, water molecules were found to play a pivotal role in stabilizing the mRNA-40S ribosome complex, underscoring their significance in this process. We successfully pinpointed the specific SARS-CoV-2 NSP1 residues that play a critical role in triggering the translation arrest.

Acetylcholinesterase inhibitory activities of some flavonoids from the root bark of Pinus krempfii Lecomte: in vitro and in silico study

From the root bark of Pinus krempfii Lecomte, four flavonoids were isolated and evaluated for their inhibitory activities against AChE and BChE enzymes in vitro and in silico. Tectochrysin (1) was found to inhibit AChE with an IC50 value of 33.69 ± 2.80 μM. The docking study results also showed agreement with the in vitro test results. All four compounds also showed the best binding affinity for the AChE enzyme, characterised by binding energy (ΔG) values as low as -8.1 to -9.3 kcal/mol, in which, the compound tectochrysin had the best binding affinity for the AChE protein with a ΔG value of -9.329 kcal/mol. Tectochrysin (1) was also bound to the amino acid Phe295 of AChE with a bond length of 2.8 Å, similar to the control dihydrotanshinone-I. Galangin (2) also showed its in vitro inhibitory activity against BChE with an IC50 value of 82.21 ± 2.70 μM. In silico, it also had the best binding energy value of -9.072 kcal/mol with BChE and formed hydrogen bonds with the His438 (2.85 Å) residues of BChE like the positive control (tacrine). The steered molecular dynamics (SMD) simulation results of these two complexes revealed a mechanistic insight that the protein-ligand complexes showed stable trajectories throughout the 20 and 150 ns simulations. Moreover, the drug likeliness suggested that both flavonoids (1 and 2) were expected to be drug-like and have an LD50 toxicity level of 5. This study has contributed new results for drug discovery and the development of substances with neuroprotective effects, especially for the treatment of Alzheimer's disease.Communicated by Ramaswamy H. Sarma.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Covalent drugs based on small molecules and peptides for disease theranostics

Biomacromolecules, such as proteins, nucleic acids and polysaccharides, are widely distributed in the human body, and some of them have been recognized as the targets of drugs for disease theranostics. Drugs typically act on targets in two ways: non-covalent bond and covalent bond. Non-covalent bond-based drugs have some disadvantages, such as structural instability and environmental sensitivity. Covalent interactions between drugs and targets have a longer action time, higher affinity and controllability than non-covalent interactions of conventional drugs. With the development of artificial intelligence, covalent drugs have received more attention and have been developed rapidly in pharmaceutical research in recent years. From the perspective of covalent drugs, this review summarizes the design methods and the effects of covalent drugs. Finally, we discuss the application of covalent peptide drugs and expect to provide a new reference for cancer treatment.

Identifying inhibitors of NSP16-NSP10 of SARS-CoV-2 from large databases

The COVID-19 pandemic, which has already claimed millions of lives, continues to pose a serious threat to human health, requiring the development of new effective drugs. Non-structural proteins of SARS-CoV-2 play an important role in viral replication and infection. Among them, NSP16 (non-structured protein 16) and its cofactor NSP10 (non-structured protein 10) perform C2'-O methylation at the 5' end of the viral RNA, which promotes efficient virus replication. Therefore, the NSP16-NSP10 complex becomes an attractive target for drug development. Using a multi-step virtual screening protocol which includes Lipinski's rule, docking, steered molecular dynamics and umbrella sampling, we searched for potential inhibitors from the PubChem and anti-HIV databases. It has been shown that CID 135566620 compound from PubChem is the best candidate with an inhibition constant in the sub-μM range. The Van der Waals interaction was found to be more important than the electrostatic interaction in the binding affinity of this compound to NSP16-NSP10. Further in vitro and in vivo studies are needed to test the activity of the identified compound against COVID-19.Communicated by Ramaswamy H. Sarma.

Ligand-receptor interaction in the specific targeting of biomimetic peptide nanoparticles to lysophosphatidylcholine

As nanotechnology is applied clinical medicine, nanoparticle-based therapy is emerging as a novel approach for the treatment of atherosclerosis. Ligand-receptor interaction affects the effectiveness of nanoparticle targeting therapy. In this study, the biomimetic peptide (BP-KFFVLK-WYKDGD) ligand specifically targeting the lysophosphatidylcholine (LPC) receptor in atherosclerotic plaques was constructed. The corresponding ligand-receptor interaction under different pH values was investigated by molecular dynamics simulation and experimental measurements. Results show that the interaction force between the peptide and LPC is greater than that of the peptide and human umbilical vein endothelial cell, clearly demonstrating the specific targeting of the peptide ligand to the LPC receptor. The ligand-receptor binding of peptide and LPC dominantly depends on Coulomb and van der Waals interactions. The YKDG amino acids of the peptide are the main fragment that binds to LPC. Compared with neutral environment at pH 7.4, the interaction forces between the peptide and oxidized low-density lipoprotein (oxLDL) decreased by 18.22 % and 45.87 % under acidic environments at pH 6.5 and 5.5, respectively, because of the change in oxLDL secondary structure and the release of LPC from oxLDL. Nevertheless, the peptide still has a strong binding capacity with oxLDL for the treatment of atherosclerosis.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Antibody-nanobody combination increases their neutralizing activity against SARS-CoV-2 and nanobody H11-H4 is effective against Alpha, Kappa and Delta variants

The global spread of COVID-19 is devastating health systems and economies worldwide. While the use of vaccines has yielded encouraging results, the emergence of new variants of SARS-CoV-2 shows that combating COVID-19 remains a big challenge. One of the most promising treatments is the use of not only antibodies, but also nanobodies. Recent experimental studies revealed that the combination of antibody and nanobody can significantly improve their neutralizing ability through binding to the SARS-CoV-2 spike protein, but the molecular mechanisms underlying this observation remain largely unknown. In this work, we investigated the binding affinity of the CR3022 antibody and H11-H4 nanobody to the SARS-CoV-2 receptor binding domain (RBD) using molecular modeling. Both all-atom steered molecular dynamics simulations and coarse-grained umbrella sampling showed that, consistent with the experiment, CR3022 associates with RBD more strongly than H11-H4. We predict that the combination of CR3022 and H11-H4 considerably increases their binding affinity to the spike protein. The electrostatic interaction was found to control the association strength of CR3022, but the van der Waals interaction dominates in the case of H11-H4. However, our study for a larger set of nanobodies and antibodies showed that the relative role of these interactions depends on the specific complex. Importantly, we showed Beta, Gamma, Lambda, and Mu variants reduce the H11-H4 activity while Alpha, Kappa and Delta variants increase its neutralizing ability, which is in line with experiment reporting that the nanobody elicited from the llama is very promising for fighting against the Delta variant.

Improving the Potential of Mean Force and Nonequilibrium Pulling Simulations by Simultaneous Alchemical Modifications

We present an approach combining alchemical modifications and physical-pathway methods to calculate absolute binding free energies. The employed physical-pathway method is either a stratified umbrella sampling to calculate a potential of mean force or nonequilibrium pulling. We devised two basic approaches: the simultaneous approach (S-approach), where, along the physical unbinding pathway, an alchemical transformation of ligand-protein interactions is installed and deinstalled, and the prior-plus-simultaneous approach (PPS-approach), where, prior to the physical-pathway simulation, an alchemical transformation of ligand-protein interactions is installed in the binding site and deinstalled during the physical-pathway simulation. Using a mutant of T4 lysozyme with a benzene ligand as an example, we show that installation and deinstallation of soft-core interactions concurrent with physical ligand unbinding (S-approach) allow successful potential of mean force calculations and nonequilibrium pulling simulations despite the problems posed by the occluded nature of the lysozyme binding pocket. Good agreement between the potential of the mean-force-based S-approach and double decoupling simulations as well as a remarkable efficiency and accuracy of the nonequilibrium-pulling-based S-approach is found. The latter turned out to be more compute-efficient than the potential of mean force calculation by approximately 70%. Furthermore, we illustrate the merits of reducing ligand-protein interactions prior to potential of mean force calculations using the murine double minute homologue protein MDM2 with a p53-derived peptide ligand (PPS-approach). Here, the problem of breaking strong interactions in the binding pocket is transferred to a prior alchemical transformation that reduces the free-energy barrier between the bound and unbound state in the potential of mean force. Besides, disentangling physical ligand displacement from the deinstallation of ligand-protein interactions was seen to allow a more uniform sampling of distance histograms in the umbrella sampling. In the future, physical ligand unbinding combined with simultaneous alchemical modifications may prove useful in the calculation of protein-protein binding free energies, where sampling problems posed by multiple, possibly sticky interactions and potential steric clashes can thus be reduced.

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.

Accelerating the discovery of the beyond rule of five compounds that have high affinities toward SARS-CoV-2 spike RBD

The battle against SARS-CoV-2 coronavirus is the focal point for the global pandemic that has affected millions of lives worldwide. The need for effective and selective therapeutics for the treatment of the disease caused by SARS-CoV-2 is critical. Herein, we performed a hierarchical computational approach incorporating molecular docking studies, molecular dynamics simulations, absolute binding energy calculations, and steered molecular dynamics simulations for the discovery of potential compounds with high affinity towards SARS-CoV-2 spike RBD. By leveraging ZINC15 database, a total of 1282 in-clinical and FDA approved drugs were filtered out from nearly 0.5 million protomers of relatively large compounds (MW > 500, and LogP ≤ 5). Our results depict plausible mechanistic aspects related to the blockage of SARS-CoV-2 spike RBD by the top hits discovered. We found that the most promising candidates, namely, ZINC95628821, ZINC95617623, ZINC3979524, and ZINC261494658, strongly bind to the spike RBD and interfere with the human ACE2 receptor. These findings accelerate the rational design of selective inhibitors targeting the spike RBD protein of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Advances in the molecular understanding of G protein-coupled receptors and their future therapeutic opportunities

Background

Understanding the mechanisms, activated and inhibited pathways as well as other molecular targets involved in existing and emerging disease conditions provides useful insights into their proper diagnosis and treatment and aids drug discovery, development and production. G protein-coupled receptors (GPCRs) are one of the most important classes of targets for small-molecule drug discovery. Of all drug targets, GPCRs are the most studied, undoubtedly because of their pharmacological tractability and role in the pathophysiology as well as the pathogenesis of human diseases.

Main body of the abstract

GPCRs are regarded as the largest target class of the “druggable genome” representing approximately 19% of the currently available drug targets. They have long played a prominent role in drug discovery, such that as of this writing, 481 drugs (about 34% of all FDA-approved drugs) act on GPCRs. More than 320 therapeutic agents are currently under clinical trials, of which a significant percentage targets novel GPCRs. GPCRs are implicated in a wide variety of diseases including CNS disorders, inflammatory diseases such as rheumatoid arthritis and Crohn’s disease, as well as metabolic disease and cancer. The non-olfactory human GPCRs yet to be clinically explored or tried are endowed with perhaps a huge untapped potential drug discovery especially in the field of immunology and genetics.

Short conclusion

This review discusses the recent advances in the molecular pharmacology and future opportunities for targeting GPCRs with a view to drug development.

Nanomechanical Stability of Aβ Tetramers and Fibril-like Structures: Molecular Dynamics Simulations

Alzheimer's disease (AD) is a neurodegenerative disorder and one of the main causes of dementia. The disease is associated with amyloid beta (Aβ) peptide aggregation forming initial clusters and then fibril structure and plaques. Other neurodegenerative diseases such as type 2 diabetes, amyotrophic lateral sclerosis, and Parkinson's disease follow a similar mechanism. Therefore, inhibition of Aβ aggregation is considered an effective way to prevent AD. Recent experiments have provided evidence that oligomers are more toxic agents than mature fibrils, prompting researchers to investigate various factors that may influence their properties. One of these factors is nanomechanical stability, which plays an important role in the self-assembly of Aβ and possibly other proteins. This stability is also likely to be related to cell toxicity. In this work, we compare the mechanical stability of Aβ-tetramers and fibrillar structures using a structure-based coarse-grained (CG) approach and all-atom molecular dynamics simulation. Our results support the evidence for an increase in mechanical stability during the Aβ fibrillization process, which is consistent with in vitro AFM characterization of Aβ42 oligomers. Namely, using a CG model, we showed that the Young modulus of tetramers is lower than that of fibrils and, as follows from the experiment, is about 1 GPa. Hydrogen bonds are the dominant contribution to the detachment of one chain from the Aβ fibril fragment. They tend to be more organized along the pulling direction, whereas in the Aβ tetramers no preference is observed.

Dependence of Work on the Pulling Speed in Mechanical Ligand Unbinding

In single-molecule force spectroscopy, the rupture force F_max required for mechanical unfolding of a biomolecule or for pulling a ligand out of a binding site depends on the pulling speed V and, in the linear Bell-Evans regime, F_max ∼ ln(V). Recently, it has been found that non-equilibrium work W is better than F_max in describing relative ligand binding affinity, but the dependence of W on V remains unknown. In this paper, we developed an analytical theory showing that in the linear regime, W ∼ c₁ ln(V) + c₂ ln²(V), where c₁ and c₂ are constants. This quadratic dependence was also confirmed by all-atom steered molecular dynamics simulations of protein-ligand complexes. Although our theory was developed for ligand unbinding, it is also applicable to other processes, such as mechanical unfolding of proteins and other biomolecules, due to its universality.

Electrostatic Interactions Explain the Higher Binding Affinity of the CR3022 Antibody for SARS-CoV-2 than the 4A8 Antibody

A structural understanding of the mechanism by which antibodies bind SARS-CoV-2 at the atomic level is highly desirable as it can tell the development of more effective antibodies to treat Covid-19. Here, we use steered molecular dynamics (SMD) and coarse-grained simulations to estimate the binding affinity of the monoclonal antibodies CR3022 and 4A8 to the SARS-CoV-2 receptor-binding domain (RBD) and SARS-CoV-2 N-terminal domain (NTD). Consistent with experiments, our SMD and coarse-grained simulations both indicate that CR3022 has a higher affinity for SARS-CoV-2 RBD than 4A8 for the NTD, and the coarse-grained simulations indicate the former binds three times stronger to its respective epitope. This finding shows that CR3022 is a candidate for Covid-19 therapy and is likely a better choice than 4A8. Energetic decomposition of the interaction energies between these two complexes reveals that electrostatic interactions explain the difference in the observed binding affinity between the two complexes. This result could lead to a new approach for developing anti-Covid-19 antibodies in which good candidates must contain charged amino acids in the area of contact with the virus.

Identification of Potential SARS-CoV-2 Main Protease and Spike Protein Inhibitors from the Genus Aloe: An In Silico Study for Drug Development

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) disease is a global rapidly spreading virus showing very high rates of complications and mortality. Till now, there is no effective specific treatment for the disease. Aloe is a rich source of isolated phytoconstituents that have an enormous range of biological activities. Since there are no available experimental techniques to examine these compounds for antiviral activity against SARS-CoV-2, we employed an in silico approach involving molecular docking, dynamics simulation, and binding free energy calculation using SARS-CoV-2 essential proteins as main protease and spike protein to identify lead compounds from Aloe that may help in novel drug discovery. Results retrieved from docking and molecular dynamics simulation suggested a number of promising inhibitors from Aloe. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) calculations indicated that compounds 132, 134, and 159 were the best scoring compounds against main protease, while compounds 115, 120, and 131 were the best scoring ones against spike glycoprotein. Compounds 120 and 131 were able to achieve significant stability and binding free energies during molecular dynamics simulation. In addition, the highest scoring compounds were investigated for their pharmacokinetic properties and drug-likeness. The Aloe compounds are promising active phytoconstituents for drug development for SARS-CoV-2.

Remdesivir Strongly Binds to Both RNA-Dependent RNA Polymerase and Main Protease of SARS-CoV-2: Evidence from Molecular Simulations

The outbreak of a new coronavirus SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) has caused a global COVID-19 (coronavirus disease 2019) pandemic, resulting in millions of infections and thousands of deaths around the world. There is currently no drug or vaccine for COVID-19, but it has been revealed that some commercially available drugs are promising, at least for treating symptoms. Among them, remdesivir, which can block the activity of RNA-dependent RNA polymerase (RdRp) in old SARS-CoV and MERS-CoV viruses, has been prescribed to COVID-19 patients in many countries. A recent experiment showed that remdesivir binds to SARS-CoV-2 with an inhibition constant of μM, but the exact target has not been reported. In this work, combining molecular docking, steered molecular dynamics, and umbrella sampling, we examined its binding affinity to two targets including the main protease (Mpro), also known as 3C-like protease, and RdRp. We showed that remdesivir binds to Mpro slightly weaker than to RdRp, and the corresponding inhibition constants, consistent with the experiment, fall to the μM range. The binding mechanisms of remdesivir to two targets differ in that the electrostatic interaction is the main force in stabilizing the RdRp-remdesivir complex, while the van der Waals interaction dominates in the Mpro-remdesivir case. Our result indicates that remdesivir can target not only RdRp but also Mpro, which can be invoked to explain why this drug is effective in treating COVID-19. We have identified residues of the target protein that make the most important contribution to binding affinity, and this information is useful for drug development for this disease.

Ranking of Ligand Binding Kinetics Using a Weighted Ensemble Approach and Comparison with a Multiscale Milestoning Approach

To improve lead optimization efforts in finding the right ligand, pharmaceutical industries need to know the ligand's binding kinetics, such as binding and unbinding rate constants, which often correlate with the ligand's efficacy in vivo. To predict binding kinetics efficiently, enhanced sampling methods, such as milestoning and the weighted ensemble (WE) method, have been used in molecular dynamics (MD) simulations of these systems. However, a comparison of these enhanced sampling methods in ranking ligands has not been done. Hence, a WE approach called the concurrent adaptive sampling (CAS) algorithm that uses MD simulations was used to rank seven ligands for β-cyclodextrin, a system in which a multiscale milestoning approach called simulation enabled estimation of kinetic rates (SEEKR) was also used, which uses both MD and Brownian dynamics simulations. Overall, the CAS algorithm can successfully rank ligands using the unbinding rate constant k_off values and binding free energy ΔG values, as SEEKR did, with reduced computational cost that is about the same as SEEKR. We compare the CAS algorithm simulations with different parameters and discuss the impact of parameters in ranking ligands and obtaining rate constant and binding free energy estimates. We also discuss similarities and differences and advantages and disadvantages of SEEKR and the CAS algorithm for future use.

Does SARS-CoV-2 Bind to Human ACE2 More Strongly Than Does SARS-CoV?

The 2019 novel coronavirus (SARS-CoV-2) epidemic, which was first reported in December 2019 in Wuhan, China, was declared a pandemic by the World Health Organization in March 2020. Genetically, SARS-CoV-2 is closely related to SARS-CoV, which caused a global epidemic with 8096 confirmed cases in more than 25 countries from 2002 to 2003. Given the significant morbidity and mortality rate, the current pandemic poses a danger to all of humanity, prompting us to understand the activity of SARS-CoV-2 at the atomic level. Experimental studies have revealed that spike proteins of both SARS-CoV-2 and SARS-CoV bind to angiotensin-converting enzyme 2 (ACE2) before entering the cell for replication. However, the binding affinities reported by different groups seem to contradict each other. Wrapp et al. (Science2020, 367, 1260–1263) showed that the spike protein of SARS-CoV-2 binds to the ACE2 peptidase domain (ACE2-PD) more strongly than does SARS-CoV, and this fact may be associated with a greater severity of the new virus. However, Walls et al. (Cell2020, 181, 281–292) reported that SARS-CoV-2 exhibits a higher binding affinity, but the difference between the two variants is relatively small. To understand the binding mechnism and experimental results, we investigated how the receptor binding domain (RBD) of SARS-CoV (SARS-CoV-RBD) and SARS-CoV-2 (SARS-CoV-2-RBD) interacts with a human ACE2-PD using molecular modeling. We applied a coarse-grained model to calculate the dissociation constant and found that SARS-CoV-2 displays a 2-fold higher binding affinity. Using steered all-atom molecular dynamics simulations, we demonstrate that, like a coarse-grained simulation, SARS-CoV-2-RBD was associated with ACE2-PD more strongly than was SARS-CoV-RBD, as evidenced by a higher rupture force and larger pulling work. We show that the binding affinity of both viruses to ACE2 is driven by electrostatic interactions.

How Good is Jarzynski's Equality for Computer-Aided Drug Design?

Accurate determination of the binding affinity of the ligand to the receptor remains a difficult problem in computer-aided drug design. Here, we study and compare the efficiency of Jarzynski’s equality (JE) combined with steered molecular dynamics and the linear interaction energy (LIE) method by assessing the binding affinity of 23 small compounds to six receptors, including β-lactamase, thrombin, factor Xa, HIV-1 protease (HIV), myeloid cell leukemia-1, and cyclin-dependent kinase 2 proteins. It was shown that Jarzynski’s nonequilibrium binding free energy ΔG_neq^Jar correlates with the available experimental data with the correlation levels R = 0.89, 0.86, 0.83, 0.80, 0.83, and 0.81 for six data sets, while for the binding free energy ΔG_LIE obtained by the LIE method, we have R = 0.73, 0.80, 0.42, 0.23, 0.85, and 0.01. Therefore, JE is recommended to be used for ranking binding affinities as it provides accurate and robust results. In contrast, LIE is not as reliable as JE, and it should be used with caution, especially when it comes to new systems.

Fast Estimation of the Blood-Brain Barrier Permeability by Pulling a Ligand through a Lipid Membrane

The blood–brain barrier (BBB) is a physical barrier that regulates the homeostasis of the neural microenvironment. A relative estimate of the BBB permeability, which is important for drug design, may be experimentally provided by the logBB (the blood–brain concentration ratio) and the logPS (permeability–surface-area product), while many computational methods aim to identify key properties that correlate well with these quantities. Although currently existing computational methods (e.g., quantitative structure activity relation) have made a significant contribution in screening various compounds that could potentially translocate through the BBB, they are unable to provide a physical explanation of the underlying processes and they can often be computationally demanding. Here, we use steered molecular dynamics simulation to estimate the BBB permeability of various compounds on the basis of simple lipid–membrane models by computing the nonequilibrium work, W_neq, produced by pulling the compounds through the membrane. We found that the values of W_neq correlate remarkably well with logBB and logPS for a range of compounds and different membrane types and pulling speeds, independently of the choice of force field. Moreover, our results provide insight into the role of hydrogen bonds, the energetic barriers, and the forces exerted on the ligands during their pulling. Our method is computationally easy to implement and fast. Therefore, we anticipate that it could provide a reliable prescreening tool for estimating the relative permeability of the BBB to various substances.

ModiBodies: A computational method for modifying nanobodies in nanobody-antigen complexes to improve binding affinity and specificity

Nanobodies are special derivatives of antibodies, which consist of single domain fragments. They have become of considerable interest as next-generation biotechnological tools for antigen recognition. They can be easily engineered due to their high stability and compact size. Nanobodies have three complementarity-determining regions, CDRs, which are enlarged to provide a similar binding surface to that of human immunoglobulins. Here, we propose a benchmark testing algorithm that uses 3D structures of already existing protein-nanobody complexes as initial structures followed by successive mutations on the CDR domains. The aim is to find optimum binding amino acids for hypervariable residues of CDRs. We use molecular dynamics simulations to compare the binding energies of the resulting complexes with that of the known complex and accept those that are improved by mutations. We use the MDM4-VH9 complex, (PDB id 2VYR), fructose-bisphosphate aldolase from Trypanosoma congolense (PDB id 5O0W) and human lysozyme (PDB id 4I0C) as benchmark complexes. By using this algorithm, better binding nanobodies can be generated in a short amount of time. We suggest that this method can complement existing immune and synthetic library-based methods, without a need for extensive experimentation or large libraries.

Perturb-Scan-Pull: A Novel Method Facilitating Conformational Transitions in Proteins

Conformational transitions in proteins facilitate precise physiological functions. Therefore, it is crucial to understand the mechanisms underlying these processes to modulate protein function. Yet, studying structural and dynamical properties of proteins is notoriously challenging due to the complexity of the underlying potential energy surfaces (PES). We have previously developed the perturbation-response scanning (PRS) method to identify key residues that participate in the communication network responsible for specific conformational transitions. PRS is based on a residue-by-residue scan of the protein to determine the subset of residues/forces which provide the closest conformational change leading to a target conformational state, inasmuch as linear response theory applies to these motions. Here, we develop a novel method to further evaluate if conformational transitions may be triggered on the PES. We aim to study functionally relevant conformational transitions in proteins by using results obtained from PRS and feeding them as inputs to steered molecular dynamics simulations. The success and the transferability of the method are evaluated on three protein systems having different complexities of motion on the PES: calmodulin, adenylate kinase, and bacterial ferric binding protein. We find that the method captures the target conformation, while providing key residues and the optimum paths with relatively low free energy profiles.

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

Understanding Effects of PAMAM Dendrimer Size and Surface Chemistry on Serum Protein Binding with Discrete Molecular Dynamics Simulations

Polyamidoamine (PAMAM) dendrimers, a class of polymeric nanoparticles (NPs) with highly-controllable sizes and surface chemistry, are promising candidates for many biomedical applications, including drug and gene delivery, imaging, and inhibition of amyloid aggregation. In circulation, binding of serum proteins with dendritic NPs renders the formation of protein corona and alters the biological identity of the NP core, which may subsequently elicit immunoresponse and cytotoxicity. Understanding the effects of PAMAM size and surface chemistry on serum protein binding is, therefore, crucial to enable their broad biomedical applications. Here, by applying atomistic discrete molecular dynamics (DMD) simulations, we first uncovered the binding of PAMAM with HSA and Ig and detailed the dependences of such binding on PAMAM size and surface modification. Compared to either anionic or cationic surfaces, modifications with neutral phosphorylcholine (PC), polyethylene glycol (PEG), and hydroxyls (OH) significantly reduced binding with proteins. The relatively strong binding between proteins and PAMAM dendrimers with charged surface groups was mainly driven by electrostatic interactions as well as hydrophobic interactions. Using steered DMD (SDMD) simulations, we conducted a force-pulling experiment in silico estimating the critical forces separating PAMAM-protein complexes and deriving the corresponding free energy barriers for dissociation. The SDMD-derived HSA-binding affinities were consistent with existing experimental measurements. Our results highlighted the association dynamics of protein-dendrimer interactions and binding affinities, whose implications range from fundamental nanobio interfacial phenomena to the development of "stealth NPs".

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

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.

Determining the Relative Binding Affinity of Ricin Toxin A Inhibitors by Using Molecular Docking and Nonequilibrium Work

Ricin is a ribosome-inactivating protein (RIP type 2) consisting of two subunits, ricin toxin A (RTA) and ricin toxin B (RTB). Because of its cytotoxicity, ricin has worried world authorities for its potential use as a chemical weapon; therefore, its inhibition is of great biotechnological interest. RTA is the target for inhibitor synthesis, and pterin derivatives are promising candidates to inhibit it. In this study, we used a combination of the molecular docking approach and fast steered molecular dynamics (SMD) to assess the correlation between nonequilibrium work, ⟨ W⟩, and the IC₅₀ for six RTA inhibitors. The results showed that molecular docking is a powerful tool to predict good bioactive poses of RTA inhibitors, and ⟨ W⟩ presented a strong correlation with IC₅₀ ( R² = 0.961). Such a profile ranked the RTA inhibitors better than the molecular docking approach. Therefore, the combination of docking and fast SMD simulation was shown to be a promising tool to distinguish RTA-active inhibitors from inactive ones and could be used as postdocking filtering approach.

Erythromycin leads to differential protein expression through differences in electrostatic and dispersion interactions with nascent proteins

The antibiotic activity of erythromycin, which reversibly binds to a site within the bacterial ribosome exit tunnel, against many gram positive microorganisms indicates that it effectively inhibits the production of proteins. Similar to other macrolides, the activity of erythromycin is far from universal, as some peptides can bypass the macrolide-obstructed exit tunnel and become partially or fully synthesized. It is unclear why, at the molecular level, some proteins can be synthesized while others cannot. Here, we use steered molecular dynamics simulations to examine how erythromycin inhibits synthesis of the peptide ErmCL but not the peptide H-NS. By pulling these peptides through the exit tunnel of the E.coli ribosome with and without erythromycin present, we find that erythromycin directly interacts with both nascent peptides, but the force required for ErmCL to bypass erythromycin is greater than that of H-NS. The largest forces arise three to six residues from their N-terminus as they start to bypass Erythromycin. Decomposing the interaction energies between erythromycin and the peptides at this point, we find that there are stronger electrostatic and dispersion interactions with the more C-terminal residues of ErmCL than with H-NS. These results suggest that erythromycin slows or stalls synthesis of ErmCL compared to H-NS due to stronger interactions with particular residue positions along the nascent protein.

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.

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.

Discovery of DNA dyes Hoechst 34580 and 33342 as good candidates for inhibiting amyloid beta formation: in silico and in vitro study

Combining Lipinski's rule with the docking and steered molecular dynamics simulations and using the PubChem data base of about 1.4 million compounds, we have obtained DNA dyes Hoechst 34580 and Hoechst 33342 as top-leads for the Alzheimer's disease. The binding properties of these ligands to amyloid beta (Aβ) fibril were thoroughly studied by in silico and in vitro experiments. Hoechst 34580 and Hoechst 33342 prefer to locate near hydrophobic regions with binding affinity mainly governed by the van der Waals interaction. By the Thioflavin T assay, it was found that the inhibition constant IC50 ≈ 0.86 and 0.68 μM for Hoechst 34580 and Hoechst 33342, respectively. This result qualitatively agrees with the binding free energy estimated using the molecular mechanic-Poisson Boltzmann surface area method and all-atom simulations with the AMBER-f99SB-ILDN force field and water model TIP3P. In addition, DNA dyes have the high capability to cross the blood brain barrier. Thus, both in silico and in vitro experiments have shown that Hoechst 34580 and 33342 are good candidates for treating the Alzheimer's disease by inhibiting Aβ formation.

Ligand binding to anti-cancer target CD44 investigated by molecular simulations

CD44 is a cell-surface glycoprotein and receptor for hyaluronan, one of the major components of the tumor extracellular matrix. There is evidence that the interaction between CD44 and hyaluronan promotes breast cancer metastasis. Recently, the molecule F-19848A was shown to inhibit hyaluronan binding to receptor CD44 in a cell-based assay. In this study, we investigated the mechanism and energetics of F-19848A binding to CD44 using molecular simulation. Using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) method, we obtained the binding free energy and inhibition constant of the complex. The van der Waals (vdW) interaction and the extended portion of F-19848A play key roles in the binding affinity. We screened natural products from a traditional Chinese medicine database to search for CD44 inhibitors. From combining pharmaceutical requirements with docking and molecular dynamics simulations, we found ten compounds that are potentially better or equal to the F-19848A ligand at binding to CD44 receptor. Therefore, we have identified new candidates of CD44 inhibitors, based on molecular simulation, which may be effective small molecules for the therapy of breast cancer.