Effect of temperature on hepatitis a virus and exploration of binding mode mechanism of phytochemicals from tinospora cordifolia: an insight into molecular docking, MM/GBSA, and molecular dynamics simulation study

The hepatitis A virus (HAV), which causes hepatitis A, is a contagious liver ailment. The infections are not specifically treated by any medications. Therefore, the development of less harmful, more effective and cost-effective antiviral agents are necessary. The present work highlighted the in-silico activity of phytocompounds from tinospora cordifolia against HAV. The binding interaction of HAV with the phytocompounds was analyzed through molecular docking. Molecular docking revealed that chasmanthin, malabarolide, menispermacide, tinosporaside, and tinosporinone compounds bind with HAV more efficiently than other compounds. Further evaluation using 100 ns molecular dynamics simulation, MM/GBSA and free energy landscape indicated that all phytocompounds studied here were found to be most promising drug candidate against hepatitis A virus. Our computational study will encourage promoting in further investigation for in vitro and in vivo clinical trials.Communicated by Ramaswamy H. Sarma.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

In silico analysis of the antidepressant fluoxetine and similar drugs as inhibitors of the human protein acid sphingomyelinase: a related SARS-CoV-2 inhibition pathway

Acid Sphingomyelinase (ASM) is a human phosphodiesterase that catalyzes the metabolism of sphingomyelin (SM) to ceramide and phosphocholine. ASM is involved in the plasma membrane cell repair and is associated with the lysosomal inner lipid membrane by nonbonding interactions. The disruption of those interaction would result in ASM release into the lysosomal lumen and consequent degradation of its structure. Furthermore, SARS-CoV-2 infection has been linked with ASM activation and with a ceramide domain formation in the outer leaflet of the plasma membrane that is thought to be crucial for the viral particles recognition by the host cells. In this study, we have explored in silico the behavior of fluoxetine and related drugs as potential inhibitors of ASM. Theoretically, these drugs would be able to overpass lysosomal membrane and reach the interactions that sustain ASM structure, breaking them and inhibiting the ASM. The analyses of docking data indicated that fluoxetine allocated mainly in the N-terminal saposin domain via nonbonding interactions, mostly of hydrophobic nature. Similar results were obtained for venlafaxine, citalopram, atomoxetine, nisoxetine and fluoxetine's main metabolite norfluoxetine. In conclusion, it was observed that the saposin allocation may be a good indicative of the drugs inhibition mechanism, once this domain is responsible for the binding of ASM to lysosomal membrane and some of those drugs have previously been reported to inhibit the phosphodiesterase by releasing its structure in the lysosomal lumen. Our MD data also provides some insight about natural ligand C18 sphingomyelin conformations on saposin.Communicated by Ramaswamy H. Sarma.

Phytochemicals from African eggplants (Solanum macrocarpon L) and Black nightshade (Solanum nigrum L) leaves as acetylcholinesterase inhibitors: an in-silico study

Acetylcholinesterase inhibitors (AChEIs) like donepezil are commonly used to treat Alzheimer's disease. AChEIs have also been considered for other therapeutic uses, such as anti-inflammatory neuroprotective agents. Consequently, the use of natural plant products as potential AChEIs can have therapeutic benefits. We previously reported the anticholinesterase properties of the phenolics and alkaloids found in the leaf extracts of two tropical plants with nutritional and ethnobotanical importance-African eggplant (Solanum macrocarpon L) and Black nightshade (Solanum nigrum L). Here, we tested the ability of both extracts to inhibit human erythrocyte AChE (an indirect mediator of pro-inflammatory cytokines production via acetylcholine degradation). We further used molecular docking and MD simulation to identify the potential molecular mechanism(s) of phenolic and alkaloid compounds as human AChEIs. Special focus was given to compounds containing the benzyl group that can establish stacking interactions similar to donepezil (a standard AChEI). Flavone-luteolin rutinosides (LR) were identified as single-binding or dual-binding AChEIs; specifically, we attributed the dual-binding LR4 and LR5 to their linked hexose moiety. This characteristic allows the dual binders to occupy the catalytic triads and the peripheral anionic subsite, while exploring the catalytic gorge. We further delineated the inhibition of human erythrocyte AChE, as the flavone common to both plant extracts-luteolin rutinosides-had positive in silico interactions with AChE. These findings suggest that phytochemicals from S. macrocarpon and S. nigrum with dual binding properties can be potential AChE inhibitors. In fact, compounds such as LR4 and LR5 should be further investigated as potential inhibitors of human AChE and may represent important natural alternatives to donepezil.Communicated by Ramaswamy H. Sarma.

Diphenyl Diselenide and SARS-CoV-2: in silico Exploration of the Mechanisms of Inhibition of Main Protease (Mpro) and Papain-like Protease (PLpro)

The SARS-CoV-2 pandemic has prompted global efforts to develop therapeutics. The main protease of SARS-CoV-2 (M^pro) and the papain-like protease (PL^pro) are essential for viral replication and are key targets for therapeutic development. In this work, we investigate the mechanisms of SARS-CoV-2 inhibition by diphenyl diselenide (PhSe)₂ which is an archetypal model of diselenides and a renowned potential therapeutic agent. The in vitro inhibitory concentration of (PhSe)₂ against SARS-CoV-2 in Vero E6 cells falls in the low micromolar range. Molecular dynamics (MD) simulations and density functional theory (DFT) calculations [level of theory: SMD-B3LYP-D3(BJ)/6-311G(d,p), cc-pVTZ] are used to inspect non-covalent inhibition modes of both proteases via π-stacking and the mechanism of covalent (PhSe)₂ + M^pro product formation involving the catalytic residue C145, respectively. The in vitro CC₅₀ (24.61 μM) and EC₅₀ (2.39 μM) data indicate that (PhSe)₂ is a good inhibitor of the SARS-CoV-2 virus replication in a cell culture model. The in silico findings indicate potential mechanisms of proteases' inhibition by (PhSe)₂; in particular, the results of the covalent inhibition here discussed for M^pro, whose thermodynamics is approximatively isoergonic, prompt further investigation in the design of antiviral organodiselenides.

Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF_1% and EF_5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF_1% and EF_5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF_1% and EF_5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

Insights into effect of the Asp25/Asp25' protonation states on binding of inhibitors Amprenavir and MKP97 to HIV-1 protease using molecular dynamics simulations and MM-GBSA calculations

The protonation states of two aspartic acids in the catalytic strands of HIV-1 protease (PR) remarkably affect bindings of inhibitors to PR. It is requisite for the design of potent inhibitors towards PR to investigate the influences of Asp25/Asp25' protonated states on dynamics behaviour of PR and binding mechanism of inhibitors to PR. In this work, molecular dynamics (MD) simulations, MM-GBSA method and principal component (PC) analysis were coupled to explore the effect of Asp25/Asp25' protonation states on conformational changes of PR and bindings of Amprenavir and MKP97 to PR. The results show that the Asp25/Asp25' protonation states exert different impacts on structural fluctuations, flexibility and motion modes of PR. Dynamics analysis verifies that Asp25/Asp25' protonated states highly affect conformational dynamics of two flaps in PR. The binding free energy calculations results suggest that the Asp25/Asp25' protonated states obviously strengthen bindings of inhibitors to PR compared to the non-protonation state. Calculations of residue-based free energy decomposition indicate that the Asp25/Asp25' protonation not only disturbs the interaction network of inhibitors with PR but also stabilizes bindings of inhibitors to PR by cancelling the electrostatic repulsive interaction. Therefore, special attentions should be paid to the Asp25/Asp25' protonation in the design of potent inhibitors towards PR.

Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of lateral pocket in enhancing the ligand affinity

The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and there is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand-bound Mpro complexes in the Protein Data Bank (PDB) describing the interactions of different fragment chemotypes within different sites of the Mpro. In this work, we performed rigorous molecular dynamics (MD) simulation of 62 reversible ligand-Mpro complexes in the PDB to gain mechanistic insights about their interactions at the atomic level. Using a total of over 3 µs long MD trajectories, we characterized different pockets in the apo Mpro structure, and analyzed the dynamic interactions and binding affinity of ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets of Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in the lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular-level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

A computational study on the interactions between a layered imine-based COF structure and selected anticancer drugs

The covalent organic frameworks (COFs) are important materials in drug delivery. Herein, the interactions between an imine-based COF with selected commercially available anticancer drugs are studied. Molecular dynamics (MD) simulation studies were used. The studies were carried out in four different temperatures to find out the impact of the temperature on the binding free energies between the drugs and COF structure. It was found that the effect of temperature on binding free energy is ignorable. Between the hydrogen bonding, electrostatic, and van der Waals interactions, the last one is the most important one to keep the drug and COF next to each other. Also, the van der Waals interaction is keeping the layers of COF next to each other to create cavities. The cavities can be loaded with different drugs and the system can be used in drug delivery systems. Based on the obtained results, the drugs that are more lipophilic prefer to adhere more strongly to the COF in comparison with hydrophilic drugs.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset

Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF_1% and nine targets at EF_5%, with the gains in enrichment being more pronounced at the EF_1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.

Binding Modes of Small-Molecule Inhibitors to the EED Pocket of PRC2

Polycomb Polycomb repressive complex 2 (PRC2) plays a key role in silencing epigenetic gene through trimethylation of lysine 27 on histone 3 (H3K27). Dysregulations of PRC2 caused by overexpression and mutations of the core subunits of PRC2 have been implicated in many cancers. The core subunits EZH1/2 are histone-lysine N-methyltransferases that function as the enzymatic component of PRC2. While the core subunit EED is a scaffolding protein to support EZH1/2 and binds JARID2K116me3/H3K27me3 to enhance the enzymatic activity of PRC2 through allosteric activation. Recently, several small molecules that compete with JARI2K116me3 and H3K27me3 have been reported. These molecules selectively bind to the JARID2K116me3/H3K27me3-binding pocket of EED, thereby preventing the allosteric regulation of PRC2. These first-in-class PRC2 inhibitors show robust suppression in DLBCL cell lines, demonstrating anticancer drugs that target the EED subunit of PRC2 are viable. In this study, we used the recently developed MM/GBSA_IE and the alanine scanning method to analyze the hot spots in EED/inhibitor interactions. The analysis of these hot and warm spots helps us to understand the fundamental differences between inhibitors. Our results give a quantitative explanation on why the binding affinities of EED/A-395 interactions are stronger than that of EED/EED226 while their binding modes are similar and provide valuable insights for rational design of novel EED inhibitors.

A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1-Ligand Binding Free Energy Calculation

Binding free energy (ΔGbind) computation can play an important role in prioritizing compounds to be evaluated experimentally on their affinity for target proteins, yet fast and accurate ΔGbind calculation remains an elusive task. In this study, we compare the performance of two popular end-point methods, i.e., linear interaction energy (LIE) and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), with respect to their ability to correlate calculated binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with the standard single-trajectory setup of MM/PBSA, our study elucidates that LIE allows to obtain direct ("absolute") values for SIRT1 binding free energies with lower compute requirements, while the accuracy in calculating relative values for ΔGbind is comparable (Pearson's r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We also investigate the potential of combining multiple docking poses in iterative LIE models and find that Boltzmann-like weighting of outcomes of simulations starting from different poses can retrieve appropriate binding orientations. In addition, we find that in this particular case study the LIE and MM/PBSA models can be optimized by neglecting the contributions from electrostatic and polar interactions to the ΔGbind calculations.

Blockade of Human α7 Nicotinic Acetylcholine Receptor by α-Conotoxin ImI Dendrimer: Insight from Computational Simulations

Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that are involved in fast synaptic transmission and mediated physiological activities in the nervous system. α-Conotoxin ImI exhibits subtype-specific blockade towards homomeric α7 and α9 receptors. In this study, we established a method to build a 2×ImI-dendrimer/h (human) α7 nAChR model, and based on this model, we systematically investigated the molecular interactions between the 2×ImI-dendrimer and hα7 nAChR. Our results suggest that the 2×ImI-dendrimer possessed much stronger potency towards hα7 nAChR than the α-ImI monomer and demonstrated that the linker between α-ImI contributed to the potency of the 2×ImI-dendrimer by forming a stable hydrogen-bond network with hα7 nAChR. Overall, this study provides novel insights into the binding mechanism of α-ImI dendrimer to hα7 nAChR, and the methodology reported here opens an avenue for the design of more selective dendrimers with potential usage as drug/gene carriers, macromolecular drugs, and molecular probes.

Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants

With the potential of improving virtual screening outcome, MM-PB/GBSA has become a disputed method that requires extensive testing and tuning to provide the optimal results. One of the tuning factors is the internal or solute dielectric constant. We have applied three test sets with receptors of different categories and libraries from different sources to investigate the underlying issue related to this constant. We discovered that increasing internal dielectric value does not improve the virtual screening enrichment qualitatively. More interestingly, nonpolar and polar calculated energies act differently in libraries with different molecular weight distributions. From this work, the performance of MM-PBSA rescoring in virtual screening is more library- than receptor-dependent.

Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures

The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction, as they provide an intermediate compromise of speed and accuracy between the empirical scoring functions used in docking and more robust free energy perturbation methods. In this study, we systematically assessed the performance of MM/PBSA in predicting experimental binding free energies using twenty Class A GPCR crystal structures and 934 known ligands. Correlations between predicted and experimental binding free energies varied significantly between individual targets, ranging from r = - 0.334 in the inactive-state CB₁ cannabinoid receptor to r = 0.781 in the active-state CB₁ cannabinoid receptor, while average correlation across all twenty targets was relatively poor (r = 0.183). MM/PBSA provided better predictions of binding free energies compared to docking scores in eight out of the twenty GPCR targets while performing worse for four targets. MM/PBSA binding affinity predictions calculated using a single, energy minimized structure provided comparable predictions to sampling from molecular dynamics simulations and may be more efficient when computational cost becomes restrictive. Additionally, we observed that restricting MM/PBSA calculations to ligands with a high degree of structural similarity to the crystal structure ligands improved performance in several cases. In conclusion, while MM/PBSA remains a valuable tool for GPCR structure-based drug design, its performance in predicting the binding free energies of GPCR ligands remains highly system-specific as demonstrated in a subset of twenty Class A GPCRs, and validation of MM/PBSA-based methods for each individual case is recommended before prospective use.

The impact of interior dielectric constant and entropic change on HIV-1 complex binding free energy prediction

At present, the calculated binding free energy obtained using the molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area (MM/PB(GB)SA) method is overestimated due to the lack of knowledge of suitable interior dielectric constants in the simulation on the interaction of Human Immunodeficiency Virus (HIV-1) protease systems with inhibitors. Therefore, the impact of different values of the interior dielectric constant and the entropic contribution when using the MM/PB(GB)SA method to calculate the binding free energy was systemically evaluated. Our results show that the use of higher interior dielectric constants (1.4-2.0) can clearly improve the predictive accuracy of the MM/PBSA and MM/GBSA methods, and computational errors are significantly reduced by including the effects of electronic polarization and using a new highly efficient interaction entropy (IE) method to calculate the entropic contribution. The suitable range for the interior dielectric constant is 1.4-1.6 for the MM/PBSA method; within this range, the correlation coefficient fluctuates around 0.84, and the mean absolute error fluctuates around 2 kcal/mol. Similarly, an interior dielectric constant of 1.8-2.0 produces a correlation coefficient of approximately 0.76 when using the MM/GBSA method. In addition, the entropic contribution of each individual residue was further calculated using the IE method to predict hot-spot residues, and the detailed binding mechanisms underlying the interactions of the HIV-1 protease, its inhibitors, and bridging water molecules were investigated. In this study, the use of a higher interior dielectric constant and the IE method can improve the calculation accuracy of the HIV-1 system.

Binding free energy analysis of protein-protein docking model structures by evERdock

To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20 and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

Chemical Modulation of the Human Oligopeptide Transporter 1, hPepT1

In humans, peptides derived from dietary proteins and peptide-like drugs are transported via the proton-dependent oligopeptide transporter hPepT1 (SLC15A1). hPepT1 is located across the apical membranes of the small intestine and kidney, where it serves as a high-capacity low-affinity transporter of a broad range of di- and tripeptides. hPepT1 is also overexpressed in the colon of inflammatory bowel disease (IBD) patients, where it mediates the transport of harmful peptides of bacterial origin. Therefore, hPepT1 is a drug target for prodrug substrates interacting with intracellular proteins or inhibitors blocking the transport of toxic bacterial products. In this study, we construct multiple structural models of hPepT1 representing different conformational states that occur during transport and inhibition. We then identify and characterize five ligands of hPepT1 using computational methods, such as virtual screening and QM-polarized ligand docking (QPLD), and experimental testing with uptake kinetic measurements and electrophysiological assays. Our results improve our understanding of the substrate and inhibitor specificity of hPepT1. Furthermore, the newly discovered ligands exhibit unique chemotypes, providing a framework for developing tool compounds with optimal intestinal absorption as well as future IBD therapeutics against this emerging drug target.

Effect of sampling on BACE-1 ligands binding free energy predictions via MM-PBSA calculations

The BACE-1 enzyme is a prime target to find a cure to Alzheimer's disease. In this article, we used the MM-PBSA approach to compute the binding free energies of 46 reported ligands to this enzyme. After showing that the most probable protonation state of the catalytic dyad is mono-protonated (on ASP32), we performed a thorough analysis of the parameters influencing the sampling of the conformational space (in total, more than 35 μs of simulations were performed). We show that ten simulations of 2 ns gives better results than one of 50 ns. We also investigated the influence of the protein force field, the water model, the periodic boundary conditions artifacts (box size), as well as the ionic strength. Amber03 with TIP3P, a minimal distance of 1.0 nm between the protein and the box edges and a ionic strength of I = 0.2 M provides the optimal correlation with experiments. Overall, when using these parameters, a Pearson correlation coefficient of R = 0.84 (R²  = 0.71) is obtained for the 46 ligands, spanning eight orders of magnitude of K_d (from 0.017 nm to 2000 μM, i.e., from -14.7 to -3.7 kcal/mol), with a ligand size from 22 to 136 atoms (from 138 to 937 g/mol). After a two-parameter fit of the binding affinities for 12 of the ligands, an error of RMSD = 1.7 kcal/mol was obtained for the remaining ligands. © 2017 Wiley Periodicals, Inc.

Exploration of 3-methylisoquinoline-4-carbonitriles as protein kinase A inhibitors of Plasmodium falciparum

A series of isoquinolines have been evaluated in a homology model of Plasmodium falciparum Protein Kinase A (PfPKA) using molecular dynamics. Synthesis of these compounds was then undertaken to investigate their structure-activity relationships. One compound was found to inhibit parasite growth in an in vitro assay and provides a lead to further develop 3-methylisoquinoline-4-carbonitriles as antimalarial compounds. Development of a potent and selective PfPKA inhibitor would provide a useful tool to shed further insight into the mechanisms enabling malaria parasites to establish infection.

Structural Study of a Flexible Active Site Loop in Human Indoleamine 2,3-Dioxygenase and Its Functional Implications

Human indoleamine 2,3-dioxygenase catalyzes the oxidative cleavage of tryptophan to N-formyl kynurenine, the initial and rate-limiting step in the kynurenine pathway. Additionally, this enzyme has been identified as a possible target for cancer therapy. A 20-amino acid protein segment (the JK loop), which connects the J and K helices, was not resolved in the reported hIDO crystal structure. Previous studies have shown that this loop undergoes structural rearrangement upon substrate binding. In this work, we apply a combination of replica exchange molecular dynamics simulations and site-directed mutagenesis experiments to characterize the structure and dynamics of this protein region. Our simulations show that the JK loop can be divided into two regions: the first region (JK loop(C)) displays specific and well-defined conformations and is within hydrogen bonding distance of the substrate, while the second region (JK loop(N)) is highly disordered and exposed to the solvent. The peculiar flexible nature of JK loop(N) suggests that it may function as a target for post-translational modifications and/or a mediator for protein-protein interactions. In contrast, hydrogen bonding interactions are observed between the substrate and Thr379 in the highly conserved "GTGG" motif of JK loop(C), thereby anchoring JK loop(C) in a closed conformation, which secures the appropriate substrate binding mode for catalysis. Site-directed mutagenesis experiments confirm the key role of this residue, highlighting the importance of the JK loop(C) conformation in regulating the enzymatic activity. Furthermore, the existence of the partially and totally open conformations in the substrate-free form suggests a role of JK loop(C) in controlling substrate and product dynamics.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter

Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the β-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepT_St, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families.

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A hierarchical computational approach determines ligand affinities to transporters

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Lysine-containing dipeptides proposed to bind vertically like a tripeptide

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Experimental structures are vital for the accurate prediction of affinities

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A model of prodrug interactions to human PepT1 is suggested

Cation–π interactions in CREBBP bromodomain inhibition: an electrostatic model for small-molecule binding affinity and selectivity

A new model is presented to explain and predict binding affinity of aromatic and heteroaromatic ligands for the CREBBP bromodomain based on cation–π interaction strength.

Molecular recognition of naphthalene diimide ligands by telomeric quadruplex-DNA: the importance of the protonation state and mediated hydrogen bonds

How important are mediated hydrogen bonds in terms of molecular recognition? Compliance Constants (relaxed force constants) give the answer.

Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F. tularensis enoyl-ACP reductase (FabI)

To validate a method for predicting the binding affinities of FabI inhibitors, three implicit solvent methods, MM-PBSA, MM-GBSA, and QM/MM-GBSA were carefully compared using 16 benzimidazole inhibitors in complex with Francisella tularensis FabI. The data suggests that the prediction results are sensitive to radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length, if only one simulation trajectory is used for each ligand. In this case, QM/MM-GBSA using 6 ns MD simulation trajectories together with GB(neck2) , PM3, and the mbondi2 radii set, generate the closest agreement with experimental values (r(2)  = 0.88). However, if the three implicit solvent methods are averaged from six 1 ns MD simulations for each ligand (called "multiple independent sampling"), the prediction results are relatively insensitive to all the tested parameters. Moreover, MM/GBSA together with GB(HCT) and mbondi, using 600 frames extracted evenly from six 0.25 ns MD simulations, can also provide accurate prediction to experimental values (r(2)  = 0.84). Therefore, the multiple independent sampling method can be more efficient than a single, long simulation method. Since future scaffold expansions may significantly change the benzimidazole's physiochemical properties (charges, etc.) and possibly binding modes, which may affect the sensitivities of various parameters, the relatively insensitive "multiple independent sampling method" may avoid the need of an entirely new validation study. Moreover, due to large fluctuating entropy values, (QM/)MM-P(G)BSA were limited to inhibitors' relative affinity prediction, but not the absolute affinity. The developed protocol will support an ongoing benzimidazole lead optimization program.

Polar Desolvation and Position 226 of Pancreatic and Neutrophil Elastases Are Crucial to their Affinity for the Kunitz-Type Inhibitors ShPI-1 and ShPI-1/K13L

The Kunitz-type protease inhibitor ShPI-1 inhibits human neutrophil elastase (HNE, K_i = 2.35·10⁻⁸ M) but does not interact with the porcine pancreatic elastase (PPE); whereas its P1 site variant, ShPI-1/K13L, inhibits both HNE and PPE (K_i = 1.3·10⁻⁹ M, and K_i = 1.2·10⁻⁸ M, respectively). By employing a combination of molecular modeling tools, e.g., structural alignment, molecular dynamics simulations and Molecular Mechanics Generalized-Born/Poisson-Boltzmann Surface Area free energy calculations, we showed that D226 of HNE plays a critical role in the interaction of this enzyme with ShPI-1 through the formation of a strong salt bridge and hydrogen bonds with K13 at the inhibitor’s P1 site, which compensate the unfavorable polar-desolvation penalty of the latter residue. Conversely, T226 of PPE is unable to establish strong interactions with K13, thereby precluding the insertion of K13 side-chain into the S1 subsite of this enzyme. An alternative conformation of K13 site-chain placed at the entrance of the S1 subsite of PPE, similar to that observed in the crystal structure of ShPI-1 in complex with chymotrypsin (PDB: 3T62), is also unfavorable due to the lack of stabilizing pair-wise interactions. In addition, our results suggest that the higher affinity of ShPI-1/K13L for both elastases mainly arises from the lower polar-desolvation penalty of L13 compared to that of K13, and not from stronger pair-wise interactions of the former residue with those of each enzyme. These results provide insights into the PPE and HNE inhibition and may contribute to the design of more potent and/or specific inhibitors toward one of these proteases.

The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities

INTRODUCTION

The molecular mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to estimate the free energy of the binding of small ligands to biological macromolecules. They are typically based on molecular dynamics simulations of the receptor-ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchemical perturbation methods. They have been applied to a large number of systems with varying success.

AREAS COVERED

The authors review the use of MM/PBSA and MM/GBSA methods to calculate ligand-binding affinities, with an emphasis on calibration, testing and validation, as well as attempts to improve the methods, rather than on specific applications.

EXPERT OPINION

MM/PBSA and MM/GBSA are attractive approaches owing to their modular nature and that they do not require calculations on a training set. They have been used successfully to reproduce and rationalize experimental findings and to improve the results of virtual screening and docking. However, they contain several crude and questionable approximations, for example, the lack of conformational entropy and information about the number and free energy of water molecules in the binding site. Moreover, there are many variants of the method and their performance varies strongly with the tested system. Likewise, most attempts to ameliorate the methods with more accurate approaches, for example, quantum-mechanical calculations, polarizable force fields or improved solvation have deteriorated the results.

On the energy components governing molecular recognition in the framework of continuum approaches

Molecular recognition is a process that brings together several biological macromolecules to form a complex and one of the most important characteristics of the process is the binding free energy. Various approaches exist to model the binding free energy, provided the knowledge of the 3D structures of bound and unbound molecules. Among them, continuum approaches are quite appealing due to their computational efficiency while at the same time providing predictions with reasonable accuracy. Here we review recent developments in the field emphasizing on the importance of adopting adequate description of physical processes taking place upon the binding. In particular, we focus on the efforts aiming at capturing some of the atomistic details of the binding phenomena into the continuum framework. When possible, the energy components are reviewed independently of each other. However, it is pointed out that rigorous approaches should consider all energy contributions on the same footage. The two major schemes for utilizing the individual energy components to predict binding affinity are outlined as well.

Comparative analysis of various electrostatic potentials on docking precision against cyclin-dependent kinase 2 protein: a multiple docking approach

The fundamental of molecular modeling is the interaction and binding to form a complex, because it explains the action of most drugs to a receptor active site. In the present study, different semiempirical (RM1, AM1, PM3, MNDO) and ab initio (HF, DFT) charge models were investigated for their performance in prediction of docking pose against CDK2 proteins with their respective inhibitor. Further, multiple docking approaches and Prime/MM-GBSA calculations were applied to predict the binding mode with respective charge model against CDK2 inhibitors. A reliable docking result was obtained using RRD, which showed significance improvement on ligand binding poses and docking score accuracy to the IFD. The combined use of RRD and Prime/MM-GBSA method could give a high correlation between the predicted binding free energy and experimental biological activity. The preliminary results point out that AM1 could be a precious charge model for design of new drugs with enhanced success rate. As a very similar result was also found for a different system of the protein-ligand binding, the suggested scoring function based on AM1 method seems to be applicable in drug design. The results from this study can provide insights into highest success rate for design of potent and selective CDK2 inhibitors.

g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations

Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), a method to estimate interaction free energies, has been increasingly used in the study of biomolecular interactions. Recently, this method has also been applied as a scoring function in computational drug design. Here a new tool g_mmpbsa, which implements the MM-PBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages is described. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks-Chandler-Andersen like integral method). We showcase the effectiveness of the tool by comparing the calculated interaction energy of 37 structurally diverse HIV-1 protease inhibitor complexes with their experimental binding free energies. The effect of varying several combinations of input parameters such as atomic radii, dielectric constant, grid resolution, solute-solvent dielectric boundary definition, and nonpolar models was investigated. g_mmpbsa can also be used to estimate the energy contribution per residue to the binding energy. It has been used to identify those residues in HIV-1 protease that are most critical for binding a range of inhibitors.

Interaction investigations of HipA binding to HipB dimer and HipB dimer + DNA complex: a molecular dynamics simulation study

We carried out molecular dynamics simulations and free energy calculations for a series of ternary and diplex models for the HipA protein, HipB dimer, and DNA molecule to address the mechanism of HipA sequestration and the binding order of events from apo HipB/HipA to 2HipA + HipB dimer + DNA complex. The results revealed that the combination of DNA with the HipB dimer is energetically favorable for the combination of HipB dimer with HipA protein. The binding of DNA to HipB dimer induces a long-range allosteric communication from the HipB2 -DNA interface to the HipA-HipB2 interface, which involves the closeness of α1 helices of HipB dimer to HipA protein and formations of extra hydrogen bonds in the HipA-HipB2 interface through the extension of α2/3 helices in the HipB dimer. These simulated results suggested that the DNA molecule, as a regulative media, modulates the HipB dimer conformation, consequently increasing the interactions of HipB dimer with the HipA proteins, which explains the mechanism of HipA sequestration reported by the previous experiment. Simultaneously, these simulations also explored that the thermodynamic binding order in a simulated physiological environment, that is, the HipB dimer first bind to DNA to form HipB dimer + DNA complex, then capturing strongly the HipA proteins to form a ternary complex, 2HipA + HipB dimer + DNA, for sequestrating HipA in the nucleoid.

Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors

The use of molecular simulation to estimate the strength of macromolecular binding free energies is becoming increasingly widespread, with goals ranging from lead optimization and enrichment in drug discovery to personalizing or stratifying treatment regimes. In order to realize the potential of such approaches to predict new results, not merely to explain previous experimental findings, it is necessary that the methods used are reliable and accurate, and that their limitations are thoroughly understood. However, the computational cost of atomistic simulation techniques such as molecular dynamics (MD) has meant that until recently little work has focused on validating and verifying the available free energy methodologies, with the consequence that many of the results published in the literature are not reproducible. Here, we present a detailed analysis of two of the most popular approximate methods for calculating binding free energies from molecular simulations, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA), applied to the nine FDA-approved HIV-1 protease inhibitors. Our results show that the values obtained from replica simulations of the same protease-drug complex, differing only in initially assigned atom velocities, can vary by as much as 10 kcal mol-1, which is greater than the difference between the best and worst binding inhibitors under investigation. Despite this, analysis of ensembles of simulations producing 50 trajectories of 4 ns duration leads to well converged free energy estimates. For seven inhibitors, we find that with correctly converged normal mode estimates of the configurational entropy, we can correctly distinguish inhibitors in agreement with experimental data for both the MMPBSA and MMGBSA methods and thus have the ability to rank the efficacy of binding of this selection of drugs to the protease (no account is made for free energy penalties associated with protein distortion leading to the over estimation of the binding strength of the two largest inhibitors ritonavir and atazanavir). We obtain improved rankings and estimates of the relative binding strengths of the drugs by using a novel combination of MMPBSA/MMGBSA with normal mode entropy estimates and the free energy of association calculated directly from simulation trajectories. Our work provides a thorough assessment of what is required to produce converged and hence reliable free energies for protein-ligand binding.

Effect of ligand binding on the dynamics of trypsin. Comparison of different approaches

The intramolecular signal transduction induced by the binding of ligands to trypsin was investigated by molecular dynamics simulations. Ligand binding changes the residue-residue interaction energies and suppresses the mobility of loops that are in direct contact with the ligand. The reduced mobility of these loops results in the altered flexibility of the nearby loops and thereby transmits the information from ligand binding site to the remote sites. The analysis of the flexibility of all residues confirmed the coupling between loops L1 (185-188) and L2 (221-224) and the residues in the active center. The significance of S1 pocket residues for the signal transduction from the active center to the substrate-binding site was confirmed by the dynamical network and covariance matrix analyses. Gaussian network model and principal component analysis demonstrated that the active center residues had zero amplitude in the slowest fluctuations acting as hinges or anchors. Overall, our results provide a new insight into protein-ligand interactions and show how the allosteric signaling may occur.