Solvent interaction energy calculations on molecular dynamics trajectories: increasing the efficiency using systematic frame selection

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Dissecting the Structural Dynamics of Authentic Cholesteryl Ester Transfer Protein for the Discovery of Potential Lead Compounds: A Theoretical Study

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions.

3-[3-(Phenalkylamino)cyclohexyl]phenols: Synthesis, biological activity, and in silico investigation of a naltrexone-derived novel class of MOR-antagonists

The development of novel μ-opioid receptor (MOR) antagonists is one of the main objectives of drug discovery and development. Based on a simplified version of the morphinan scaffold, 3-[3-(phenalkylamino)cyclohexyl]phenol analogs were designed, synthesized, and evaluated for their MOR antagonist activity in vitro and in silico. At the highest concentrations, the compounds decreased by 52% to 75% DAMGO-induced GTPγS stimulation, suggesting that they acted as antagonists. Moreover, Extra-Precision Glide and Generalized-Born Surface Area experiments provided useful information on the nature of the ligand-receptor interactions, indicating a peculiar combination of C-1 stereochemistry and N-substitutions as feasibly essential for MOR-ligand complex stability. Interestingly, compound 9 showed the best experimental binding affinity, the highest antagonist activity, and the finest MOR-ligand complex stability. In silico experiments also revealed that the most promising stereoisomer (1R, 3R, 5S) 9 retained 1,3-cis configuration with phenol ring equatorial oriented. Further studies are needed to better characterize the pharmacodynamics and pharmacokinetic properties of these compounds.

A study of possible substitutes for the endocrine disruptor DEHP in two hormone receptors

Bis(2-ethylhexyl) phthalate (DEHP) has been widely used for the production of plastics, and the compound has also been found to act as endocrine disruptor. Exposure to DEHP has been found to cause several hormonal problems, including decreased fertility. Due to the environmental and health risks posed by the use of DEHP, the present study employed molecular docking, molecular dynamics, and free energy analyses (MM-GBSA, MM-PBSA, and SIE) aiming at evaluating the action of DEHP and that of two other compounds (ATEC and DL9TH), tested as potential DEHP substitutes, on two hormone receptors (sex hormone-binding globulin - SHBG - and progesterone receptor - PR). The results obtained showed that ATEC may be a good substitute for DEHP in the production of plastics, such as PVC, considering that the compound recorded the greatest free energy values with respect to binding with SHBG (-31.36 kcal/mol obtained from MM-GBSA; -20.28 kcal/mol for MM-PBSA, and -7.40 for SIE) and PR (-36.40 kcal/mol for MM-GBSA; -27.00 kcal/mol for MM-PBSA, and -8.51 kcal/mol for SIE) - this shows that ATEC presented the least activity in the two hormone receptors. The findings of this study provide relevant insights on potential substitutes for DEHP and help shed light on the action of these new efficient substances, which have similar properties to DEHP (ATEC and DL9TH) yet do not act as endocrine disruptors.Communicated by Ramaswamy H. Sarma.

Assessment of mutations on RBD in the Spike protein of SARS-CoV-2 Alpha, Delta and Omicron variants

The severe acute respiratory syndrome (SARS) coronavirus 2 (CoV-2) variant Omicron spread more rapid than the other variants of SARS-CoV-2 virus. Mutations on the Spike (S) protein receptor-binding domain (RBD) are critical for the antibody resistance and infectivity of the SARS-CoV-2 variants. In this study, we have used accelerated molecular dynamics (aMD) simulations and free energy calculations to present a systematic analysis of the affinity and conformational dynamics along with the interactions that drive the binding between Spike protein RBD and human angiotensin-converting enzyme 2 (ACE2) receptor. We evaluate the impacts of the key mutation that occur in the RBDs Omicron and other variants in the binding with the human ACE2 receptor. The results show that S protein Omicron has stronger binding to the ACE2 than other variants. The evaluation of the decomposition energy per residue shows the mutations N440K, T478K, Q493R and Q498R observed in Spike protein of SARS-CoV-2 provided a stabilization effect for the interaction between the SARS-CoV-2 RBD and ACE2. Overall, the results demonstrate that faster spreading of SARS-CoV-2 Omicron may be correlated with binding affinity of S protein RBD to ACE2 and mutations of uncharged residues to positively charged residues such as Lys and Arg in key positions in the RBD.

Structural, energetic and lipophilic analysis of SARS-CoV-2 non-structural protein 9 (NSP9)

In SARS-CoV-2 replication complex, the Non-structural protein 9 (Nsp9) is an important RNA binding subunit in the RNA-synthesizing machinery. The dimeric forms of coronavirus Nsp9 increase their nucleic acid binding affinity and the N-finger motif appears to play a critical role in dimerization. Here, we present a structural, lipophilic and energetic study about the Nsp9 dimer of SARS-CoV-2 through computational methods that complement hydrophobicity scales of amino acids with molecular dynamics simulations. Additionally, we presented a virtual N-finger mutation to investigate whether this motif contributes to dimer stability. The results reveal for the native dimer that the N-finger contributes favorably through hydrogen bond interactions and two amino acids bellowing to the hydrophobic region, Leu45 and Leu106, are crucial in the formation of the cavity for potential drug binding. On the other hand, Gly100 and Gly104, are responsible for stabilizing the α-helices and making the dimer interface remain stable in both, native and mutant (without N-finger motif) systems. Besides, clustering results for the native dimer showed accessible cavities to drugs. In addition, the energetic and lipophilic analysis reveal that the higher binding energy in the native dimer can be deduced since it is more lipophilic than the mutant one, increasing non-polar interactions, which is in line with the result of MM-GBSA and SIE approaches where the van der Waals energy term has the greatest weight in the stability of the native dimer. Overall, we provide a detailed study on the Nsp9 dimer of SARS-CoV-2 that may aid in the development of new strategies for the treatment and prevention of COVID-19.

Prediction of Drug Potencies of BACE1 Inhibitors: A Molecular Dynamics Simulation and MM_GB(PB)SA Scoring

Alzheimer’s disease (AD) is a progressive neurodegenerative brain disorder. One of the important therapeutic approaches of AD is the inhibition of β-site APP cleaving enzyme-1 (BACE1). This enzyme plays a central role in the synthesis of the pathogenic β-amyloid peptides (Aβ) in Alzheimer’s disease. A group of potent BACE1 inhibitors with known X-ray structures (PDB ID 5i3X, 5i3Y, 5iE1, 5i3V, 5i3W, 4LC7, 3TPP) were studied by molecular dynamics simulation and binding energy calculation employing MM_GB(PB)SA. The calculated binding energies gave Kd values of 0.139 µM, 1.39 nM, 4.39 mM, 24.3 nM, 1.39 mM, 29.13 mM, and 193.07 nM, respectively. These inhibitors showed potent inhibitory activities in enzymatic and cell assays. The Kd values are compared with experimental values and the structures are discussed in view of the energy contributions to binding. Drug likeness of these inhibitors is also discussed. Accommodation of ligands in the catalytic site of BACE1 is discussed depending on the type of fragment involved in each structure. Molecular dynamics (MD) simulations and energy studies were used to explore the recognition of the selected BACE1 inhibitors by Asp32, Asp228, and the hydrophobic flap. The results show that selective BACE1 inhibition may be due to the formation of strong electrostatic interactions with Asp32 and Asp228 and a large number of hydrogen bonds, in addition to π–π and van der Waals interactions with the amino acid residues located inside the catalytic cavity. Interactions with the ligands show a similar binding mode with BACE1. These results help to rationalize the design of selective BACE1 inhibitors.

Cyclic Peptide Inhibitors of the Tsg101 UEV Protein Interactions Refined through Global Docking and Gaussian Accelerated Molecular Dynamics Simulations

Tsg101 UEV domain proteins are potential targets for virus infection therapy, especially for HIV and Ebola viruses. Peptides are key in curbing virus transmission, and cyclic peptides have a greater survival time than their linear peptides. To date, the accurate prediction of cyclic peptide-protein receptors binding conformations still is challenging because of high peptide flexibility. Here, a useful approach combined the global peptide docking, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF), normal molecular dynamics (cMD), and solvated interaction energy (SIE) techniques. Then we used this approach to investigate the binding conformations of UEV domain proteins with three cyclic peptides inhibitors. We reported the possible cyclic peptide-UEV domain protein binding conformations via 2D PMF free energy profiles and SIE free energy calculations. The residues Trp145, Tyr147, and Trp148 of the native cyclic peptide (CP1) indeed play essential roles in the cyclic peptides-UEV domain proteins interactions. Our findings might increase the accuracy of cyclic peptide-protein conformational prediction, which may facilitate cyclic peptide inhibitor design. Our approach is expected to further aid in addressing the challenges in cyclic peptide inhibitor design.

Computational modeling of cyclic peptide inhibitor-MDM2/MDMX binding through global docking and Gaussian accelerated molecular dynamics simulations

MDM2 and MDMX are potential targets for p53-dependent cancer therapy. Peptides are key in cellular immunology and oncology, and cyclic peptides generally have higher half-life than their linear counterparts. However, prediction of cyclic peptide-protein binding is challenging with normal molecular simulation approaches because of high peptide flexibility. Here, we used global peptide docking, normal molecular dynamics, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF) profiles, and solvated interaction energy (SIE) techniques to investigate the interactions of MDM2/MDMX with three N-to-C-terminal cyclic peptide-based inhibitors. We determined the possible cyclic peptide-MDM2/MDMX complex structures via 2D PMF profiles and SIE calculations. Our findings increase the accuracy of peptide-protein structural prediction, which may facilitate cyclic peptide drug design. Advancements in the computational methods and computing power may further aid in addressing the challenges in cyclic peptide drug design. Communicated by Ramaswamy H. Sarma.

Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites

In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein-ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein-ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

Exploring the structural basis of the selective inhibition of monoamine oxidase A by dicarbonitrile aminoheterocycles: role of Asn181 and Ile335 validated by spectroscopic and computational studies

Since cyanide potentiates the inhibitory activity of several monoamine oxidase (MAO) inhibitors, a series of carbonitrile-containing aminoheterocycles was examined to explore the role of nitriles in determining the inhibitory activity against MAO. Dicarbonitrile aminofurans were found to be potent, selective inhibitors against MAO A. The origin of the MAO A selectivity was identified by combining spectroscopic and computational methods. Spectroscopic changes induced in MAO A by mono- and dicarbonitrile inhibitors were different, providing experimental evidence for distinct binding modes to the enzyme. Similar differences were also found between the binding of dicarbonitrile compounds to MAO A and to MAO B. Stabilization of the flavin anionic semiquinone by monocarbonitrile compounds, but destabilization by dicarbonitriles, provided further support to the distinct binding modes of these compounds and their interaction with the flavin ring. Molecular modeling studies supported the role played by the nitrile and amino groups in anchoring the inhibitor to the binding cavity. In particular, the results highlight the role of Asn181 and Ile335 in assisting the interaction of the nitrile-containing aminofuran ring. The network of interactions afforded by the specific attachment of these functional groups provides useful guidelines for the design of selective, reversible MAO A inhibitors.

Ponatinib is a pan-BCR-ABL kinase inhibitor: MD simulations and SIE study

BCR-ABL kinase domain inhibition can be used to treat chronic myeloid leukemia. The inhibitors such as imatinib, dasatinib and nilotinib are effective drugs but are resistant to some BCR-ABL mutations. The pan-BCR-ABL kinase inhibitor ponatinib exhibits potent activity against native, T315I, and all other clinically relevant mutants, and showed better inhibition than the previously known inhibitors. We have studied the molecular dynamics simulations and calculated solvated interaction energies of native and fourteen mutant BCR-ABL kinases (M244V, G250E, Q252H, Y253F, Y253H, E255K, E255V, T315A, T315I, F317L, F317V, M351T, F359V and H396P) complexed with ponatinib. These studies revealed that the interactions between ponatinib and individual residues in BCR-ABL kinase are also affected due to the remote residue mutations. We report that some residues, Met244, Lys245, Gln252, Gly254, Leu370 and Leu298 do not undergo any conformational changes, while the fluctuations in residues from P-loop, β3-, β5- strands and αC- helix are mainly responsible for ponatinib binding to native and all mutant BCR-ABL kinases. Our work provides the molecular mechanisms of native and mutant BCR-ABL kinases inhibition by ponatinib at atomic level that has not been studied before.

Molecular recognition in a diverse set of protein-ligand interactions studied with molecular dynamics simulations and end-point free energy calculations

End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal·mol(-1) highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10), but larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the nonpolar and entropy components, rather than a better representation of the electrostatics. The SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal-mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo, and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by prescoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the nonpolar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.

Molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches

The binding free energies (ΔG(Bind)) obtained from molecular mechanics with Poisson-Boltzmann surface area (MM-PBSA) or molecular mechanics with Generalized Born surface area (MM-GBSA) calculations using molecular dynamics (MD) trajectories are the most popular procedures to measure the strength of interactions between a ligand and its receptor. Several attempts have been made to correlate the ΔG(Bind) and experimental IC(50) values in order to observe the relationship between binding strength of a ligand (with its receptor) and its inhibitory activity. The duration of MD simulations seems very important for getting acceptable correlation. Here, we are presenting a systematic study to estimate the reasonable MD simulation time for acceptable correlation between ΔG(Bind) and experimental IC(50) values. A comparison between MM-PBSA and MM-GBSA approaches is also presented at various time scales. MD simulations (10 ns) for 14 HIV protease inhibitors have been carried out by using the Amber program. MM-PBSA/GBSA based ΔG(Bind) have been calculated and correlated with experimental IC(50) values at different time scales (0-1 to 0-10 ns). This study clearly demonstrates that the MM-PBSA based ΔG(Bind) (ΔG(Bind)-PB) values provide very good correlation with experimental IC(50) values (quantitative and qualitative) when MD simulation is carried out for a longer time; however, MM-GBSA based ΔG(Bind) (ΔG(Bind)-GB) values show acceptable correlation for shorter time of simulation also. The accuracy of ΔG(Bind)-PB increases and ΔG(Bind)-GB remains almost constant with the increasing time of simulation.

Variability in docking success rates due to dataset preparation

The results of cognate docking with the prepared Astex dataset provided by the organizers of the "Docking and Scoring: A Review of Docking Programs" session at the 241st ACS national meeting are presented. The MOE software with the newly developed GBVI/WSA dG scoring function is used throughout the study. For 80 % of the Astex targets, the MOE docker produces a top-scoring pose within 2 Å of the X-ray structure. For 91 % of the targets a pose within 2 Å of the X-ray structure is produced in the top 30 poses. Docking failures, defined as cases where the top scoring pose is greater than 2 Å from the experimental structure, are shown to be largely due to the absence of bound waters in the source dataset, highlighting the need to include these and other crucial information in future standardized sets. Docking success is shown to depend heavily on data preparation. A "dataset preparation" error of 0.5 kcal/mol is shown to cause fluctuations of over 20 % in docking success rates.

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.