Binding Free Energy Decomposition Research Articles

Molecular Generation, QSAR, and Molecular Dynamic Simulation Studies for Virtual Screening of DNA Polymerase Theta Inhibitors.

The machine learning-based QSAR modeling procedure, molecular generations, and molecular dynamic simulations were applied to virtually screen the DNA polymerase theta inhibitors. The DNA polymerase theta (Polθ or POLQ) is an attractive target for treatments of homologous recombination deficient (such as BRCA deficient) cancers. There are no approved drugs for targeting POLQ, and only one inhibitor is in Phase Ⅱclinical trials; thus, it is necessary to develop novel POLQ inhibitors. To build machine learning models that predict the bioactivities of POLQ inhibitors. To build molecular generation models that generate diverse molecules. To virtually screen the generated molecules by the machine learning models. To analyze the binding modes of the screening results by molecular dynamic simulations. In the present work, 325 inhibitors with POLQ polymerase domain bioactivities were Collected. Two machine learning methods, random forest and deep neural network, were used for building the ligand- and structure-based quantitative structure-activity relationship (QSAR) models. The substructure replacement-based method and transfer learning-based deep recurrent neural network method were used for molecular generations. Molecular docking and consensus QSAR models were carried out for virtual screening. The molecular dynamic simulations and MM/GBSA binding free energy calculation and decomposition were used to further analyze the screening results. The MCC values of the best ligand- and structure-based consensus QSAR models reached 0.651 and 0.361 for the test set, respectively. The machine learning-based docking scores had better-predicted ability to distinguish the highly and weakly active poses than the original docking scores. The 96490 molecules were generated by both molecular generation methods, and 10 molecules were retained by virtual screening. Four favorable interactions were concluded by molecular dynamic simulations. We hope that the screening results and the binding modes are helpful for designing the highly active POLQ polymerase inhibitors and the models of the molecular design workflow can be used as reliable tools for drug design.

Molecular docking, 3D-QASR and molecular dynamics simulations of benzimidazole Pin1 inhibitors.

Pin1 (protein interacting with never-in-mitosis akinase-1) is a member of the family of peptidylprolyl cis-trans isomerases (PPIases) that specifically recognize and isomerize substrates containing phosphorylated Ser/Thr-Pro sequences. Pin1 is involved in many cellular processes and plays a key role in the cell cycle, transcriptional regulation, cell metabolism, proliferation and differentiation, and its abnormalities lead to degenerative and neoplastic diseases. Pin1 is highly expressed in human cancers and promotes the development of tumors by activating multiple oncogenes and inactivating multiple tumor suppressor genes, making it an attractive target for cancer therapy. In this study, we investigated the binding mechanism and conformational relationship between benzimidazole Pin1 inhibitors and Pin1 proteins by molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, binding free energy calculations and decomposition, and molecular dynamics simulations. Molecular docking and molecular dynamics simulations disclosed the most likely binding pose of benzimidazoles with the Pin1 protein. The results of 3D-QSAR modeling indicated that electrostatic fields, hydrophobic fields and hydrogen bonding play important roles in the binding process of inhibitors to proteins. The binding free energy calculations and energy decomposition indicated that Lys63, Arg69, Cys113, Leu122, Met130, and Ser154 may be key residues in the binding of benzimidazole-based inhibitors to the Pin1 protein. This study provides an important theoretical basis for the design and optimization of benzimidazole compounds.

Elucidating the conformational dynamics of histo-blood group antigens and their interactions with the rotavirus spike protein through computational lens

In the present study, we investigated the conformational dynamics of histo-blood group antigens (HBGAs) and their interactions with the VP8* domain of four rotavirus genotypes (P[4], P[6], P[19], and P[11]) utilizing all-atom molecular dynamics simulations in explicit water. Our study revealed distinct changes in the dynamic behavior of the same glycan due to linkage variations. We observed that LNFPI HBGA having a terminal β linkage shows two dominant conformations after complexation, whereas only one was obtained for LNFPI with a terminal α linkage. Interestingly, both variants displayed a single dominant structure in the free state. Similarly, LNT and LNnT show a shift in their dihedral linkage profile between their two terminal monosaccharides because of a change in the linkage from β(1–3) to β(1–4). The molecular mechanics generalized Born surface area (MM/GBSA) calculations yielded the highest binding affinity for LNFPI(β)/P[6] (−13.93 kcal/mol) due to the formation of numerous hydrogen bonds between VP8* and HBGAs. LNnT binds more strongly to P[11] (−12.88 kcal/mol) than LNT (−4.41 kcal/mol), suggesting a single change in the glycan linkage might impact its binding profile significantly. We have also identified critical amino acids and monosaccharides (Gal and GlcNAc) that contributed significantly to the protein-ligand binding through the per-residue decomposition of binding free energy. Moreover, we found that the interaction between the same glycan and different protein receptors within the same rotavirus genogroup influenced the micro-level dynamics of the glycan. Overall, our study helps a deeper understanding of the H-type HBGA and rotavirus spike protein interaction. Communicated by Ramaswamy H. Sarma

Computational Assessment of Cannflavin A as a TAK1 Inhibitor: Implication as a Potential Therapeutic Target for Anti-Inflammation

TAK1 (transforming growth factor-beta-activated kinase 1) is a crucial therapeutic target in inflammation-related diseases. This study investigated the inhibitory potential of cannflavin A, a flavonoid found in Cannabis sativa, against TAK1. Through in silico approaches, including drug-likeness analysis, ADMET assessment, molecular docking, and molecular dynamics simulation, the binding affinity and stability of cannflavin A were evaluated. The results demonstrate that cannflavin A exhibits excellent ADMET properties and displays superior binding affinity and stability at the ATP binding site of TAK1 when compared to the known inhibitor takinib. Notably, the decomposition of binding free energy unveils critical amino acid residues involved in TAK1 binding, underscoring the inhibitory effect of cannflavin A through TAK1 inhibition. These findings highlight the potential of cannflavin A as a TAK1 inhibitor and its significant implications for the development of targeted therapies in inflammation-related diseases. Through modulating inflammatory signaling pathways, cannflavin A holds promise for more effective and tailored treatment strategies, particularly in rheumatoid arthritis. This study contributes to the current understanding of cannflavin A’s application and provides a foundation for further research and innovative approaches in targeted therapies for inflammatory conditions.

Insights Into Resveratrol as an Inhibitor Against Aβ1-42 Peptide Aggregation: A Molecular Dynamics Simulation Study

Background: Resveratrol (RSV), a polyphenolic compound, is reported to have anti-aggregation properties against Amyloid-beta peptides. It is, therefore, significant to understand the mechanism of inhibition of Aβ1-42 peptide aggregation by the RSV at the molecular level. We have used Molecular docking along with Molecular dynamics (MD) simulation techniques to address the role of RSV in the inhibition of Aβ1-42 pep-tide aggregation. Objective: To understand the role of Resveratrol on the Aβ1-42 peptide aggregation. Method: In this computational study, we have docked the RSV to Aβ1-42 peptide using Molecular Docking software and then performed MD simulation for the Aβ1-42 peptide monomer Aβ1-42 peptide-RSV complex using the AMBER force field. From the analysis of MD trajectories, we obtained salient structural features and determined the Binding Free Energy(BFE) and Per-residue Energy Decomposition Analysis (PRED) using MM-PBSA/GBSA method. Results: The secondary structure and the conformational analysis obtained from MD trajectories show that the binding of RSV with the Aβ1-42 peptide monomer causes an increase in the helical content in the structure of the Aβ1-42 peptide. The BFE and PRED results show a high binding affinity (GBtotal=-11.07 kcal mol-1; PBtotal= -1.82 kcal mol-1) of RSV with Aβ1-42 peptide. Also, we found the RSV to interact with cru-cial residues (Asp 23 and Lys 28) of the Aβ1-42 peptide. These residues play a signif-icant role in facilitating the formation of toxic amyloid oligomers and amyloid fibrils. The salt bridge interaction between these residues D23–K28 was found to be destabi-lized in the Aβ1-42 peptide when it is complexed with RSV. Conclusion: In summary, it can be concluded that Resveratrol greatly aids the preven-tion of Aβ1-42 peptide aggregation. Therefore, it can be considered a possible drug candidate for therapeutic strategies for Alzheimer’s disease.

Dynamical characterization and multiple unbinding paths of two PreQ1 ligands in one pocket.

Riboswitches naturally regulate gene expression in bacteria by binding to specific small molecules. Class 1 preQ1 riboswitch aptamer is an important model not only for RNA folding but also as a target for designing small molecule antibiotics due to its well-known minimal aptamer domain. Here, we ran a total of 62.4 μs conventional and enhanced-sampling molecular dynamics (MD) simulations to characterize the determinants underlying the binding of the preQ1-II riboswitch aptamer to two preQ1 ligands in one binding pocket. Decomposition of binding free energy suggested that preQ1 ligands at α and β sites interact with four nucleotides (G5, C17, C18, and A30) and two nucleotides (A12 and C31), respectively. Mg2+ ions play a crucial role in both stabilizing the binding pocket and facilitating ligand binding. The flexible preQ1 ligand at the β site leads to the top of the binding pocket loosening and thus pre-organizes the riboswitch for ligand entry. Enhanced sampling simulations further revealed that the preQ1 ligand at the α site unbinds through two orthogonal pathways, which are dependent on whether or not a β site preQ1 ligand is present. One of the two preQ1 ligands has been identified in the binding pocket, which will aid to identify the second preQ1 Ligand. Our work provides new information for designing robust ligands.

Identification of potential inhibitors of omicron variant of SARS-Cov-2 RBD based virtual screening, MD simulation, and DFT.

Emergence of the SARS-CoV-2 Omicron variant of concern (VOC; B.1.1.529) resulted in a new peak of the COVID-19 pandemic, which called for development of effective therapeutics against the Omicron VOC. The receptor binding domain (RBD) of the spike protein, which is responsible for recognition and binding of the human ACE2 receptor protein, is a potential drug target. Mutations in receptor binding domain of the S-protein have been postulated to enhance the binding strength of the Omicron VOC to host proteins. In this study, bioinformatic analyses were performed to screen for potential therapeutic compounds targeting the omicron VOC. A total of 92,699 compounds were screened from different libraries based on receptor binding domain of the S-protein via docking and binding free energy analysis, yielding the top 5 best hits. Dynamic simulation trajectory analysis and binding free energy decomposition were used to determine the inhibitory mechanism of candidate molecules by focusing on their interactions with recognized residues on receptor binding domain. The ADMET prediction and DFT calculations were conducted to determine the pharmacokinetic parameters and precise chemical properties of the identified molecules. The molecular properties of the identified molecules and their ability to interfere with recognition of the human ACE2 receptors by receptor binding domain suggest that they are potential therapeutic agents for SARS-CoV-2 Omicron VOC.

Evaluation of interactions between the hepatitis C virus NS3/4A and sulfonamidobenzamide based molecules using molecular docking, molecular dynamics simulations and binding free energy calculations.

The Hepatitis C Virus (HCV) NS3/4A is an attractive target for the treatment of Hepatitis C infection. Herein, we present an investigation of HCV NS3/4A inhibitors based on a sulfonamidobenzamide scaffold. Inhibitor interactions with HCV NS3/4A were explored by molecular docking, molecular dynamics simulations, and MM/PBSA binding free energy calculations. All of the inhibitors adopt similar molecular docking poses in the catalytic site of the protease that are stabilized by hydrogen bond interactions with G137 and the catalytic S139, which are known to be important for potency and binding stability. The quantitative assessments of binding free energies from MM/PBSA correlate well with the experimental results, with a high coefficient of determination, R2 of 0.92. Binding free energy decomposition analyses elucidate the different contributions of Q41, F43, H57, R109, K136, G137, S138, S139, A156, M485, and Q526 in binding different inhibitors. The importance of these sidechain contributions was further confirmed by computational alanine scanning mutagenesis. In addition, the sidechains of K136 and S139 show crucial but distinct contributions to inhibitor binding with HCV NS3/4A. The structural basis of the potency has been elucidated, demonstrating the importance of the R155 sidechain conformation. This extensive exploration of binding energies and interactions between these compounds and HCV NS3/4A at the atomic level should benefit future antiviral drug design.

Discovery of novel inhibitors targeting nematode chitinase CeCht1: Virtual screening, biological evaluation, and molecular dynamics simulation.

Plant-parasitic nematodes are a main limiting factor for worldwide agriculture. To reduce the global burden of nematode infections, chemical nematicides are still the most effective methods to manage nematodes. With the increasing resistance of nematodes, the development of new anti-nematicides drug is urgent. Nematode chitinases are found to play important roles in various physiological functions, such as larva moulting, hatching from eggshell, and host infection. Inhibition of nematode chitinase is considered a promising strategy for the development of eco-friendly nematicides. In this study, to develop novel nematode chitinase CeCht1 inhibitors, virtual screening of the ZINC database was performed using the pesticide-likeness rules, pharmacophore-based and docking-based approach in turn. Compounds HAU-4 and HAU-7 were identified as potent CeCht1 inhibitors with the IC50 values of 4.2μM and 10.0μM, respectively. Moreover, molecular dynamics simulations combined with binding free energy and free energy decomposition calculations were conducted to investigate the basis for the potency of the two inhibitors toward CeCht1. This work gives an insight into the future rational development of novel and potent nematode chitinase inhibitors.

The effect of genotype variation and M423 resistance mutations to the binding of phosphonomidate-based inhibitor IDX17119 with the thumb-II domain of Hepatitis C virus RdRp: an integrated molecular dynamics and binding free energy study

ABSTRACT The IDX17119 is a phosphonomidate-based pan-genotypic non-nucleoside inhibitor of Hepatitis C Virus (HCV) NS5B RdRp thumb-II domain. IDX17119 exhibits significant antiviral activity against HCV genotype (GT) 1a, 2a, 3a and 4a. The molecular docking analysis was performed to understand the binding and intermolecular interactions of the IDX17119 molecule at the thumb-II domain of 1a, 2a, 3a and 4a GT NS5B RdRps. The molecular dynamics simulation has been performed to determine the stability of the ligand–protein complexes. We found GT 1a complex is relatively stable compared with other drug-receptor complexes. The binding free energy calculations and free energy decomposition analysis were carried out for all the complexes, the result revealed that the increase in the unfavourable polar contribution affects the binding of IDX17119 with GT 2a NS5B RdRp compared with all other complexes. In addition to this, the effect of mutation at M423 site (M423T/V) alters the conformation and activity of the IDX17119 molecule.

Heterologous expression of family GH11 Aspergillus niger xylanase B (AnXylB11) in Pichia pastoris and competitive inhibition by riceXIP: An experimental and simulation study

The family GH11 Aspergillus niger JL15 xylanase B (AnXylB11) was heterologously expressed in Pichia pastoris X33. The recombinant AnXylB11 (reAnXylB11) was secreted into the culture medium with a molecular weight of approximately 33.0 kDa. The optimal temperature and pH of reAnXylB11 were 40 ℃ and 5.0, respectively. reAnXylB11 released xylobiose (X2)-xylohexaose (X6) from beechwood xylan, with xylotriose (X3) as the primary product. The hydrolysates showed significant antioxidant activity. reAnXylB11 was also competitively inhibited by recombinant rice xylanase inhibitory protein (rePriceXIP), with an inhibition constant (Ki) of 106.9 nM. Molecular dynamics (MD) simulations, non-covalent interactions (NCI), and binding free energy calculation and decomposition were conducted to decipher the interactional features between riceXIP and AnXyB11. Representative conformation of riceXIP-AnXylB11 complex showed that a U-shaped long loop between α4 and β5 (K143-L152) of riceXIP was protruded into the catalytic groove and formed tight interaction with many key residues of AnXylB11. The binding free energy of riceXIP-AnXylB11 was calculated to be − 46.1 ± 10.5 kcal/mol, with Coulomb and van der Waals forces contributing the most. NCI analysis showed that the hydrogen bonding networks such as R148riceXIP-E98AnXyl11B, K143riceXIP-D138AnXyl11B and R148riceXIP-E189AnXyl11B provided terrific contributions to the interface interaction. The Laplacian of electron density values of atom pairs R148riceXIP@ 2HH1-E89AnXylB11@OE2 and N142riceXIP@ 1HD2-D138AnXylB11@OD1 were 0.12190 and 0.16009 a.u., respectively. Exploring the interactional features between xylanase and inhibitor protein may aid in constructing mutant xylanase that is insensitive to xylanase inhibitory proteins (XIs).

A comparative study of receptor interactions between SARS-CoV and SARS-CoV-2 from molecular modeling

The pandemic of COVID-19 severe acute respiratory syndrome, which was fatal for millions of people worldwide, triggered the race to understand in detail the molecular mechanisms of this disease. In this work, the differences of interactions between the SARS-CoV/SARS-CoV-2 Receptor binding domain (RBD) and the human Angiotensin Converting Enzyme 2 (ACE2) receptor were studied using in silico tools. Our results show that SARS-CoV-2 RBD is more stable and forms more interactions with ACE2 than SARS-CoV. At its interface, three stable binding patterns are observed and named red-K31, green-K353 and blue-M82 according to the central ACE2 binding residue. In SARS-CoV instead, only the first two binding patches are persistently formed during the MD simulation. Our MM/GBSA calculations indicate the binding free energy difference of about 2.5 kcal/mol between SARS-CoV-2 and SARS-CoV which is compatible with the experiments. The binding free energy decomposition points out that SARS-CoV-2 RBD–ACE2 interactions of the red-K31 (-23.5~pm ~0.2~kcal/mol) and blue-M82 (-9.1~pm ~0.1~kcal/mol) patterns contribute more to the binding affinity than in SARS-CoV (-1.8~pm ~0.02~kcal/mol for red-K31), while the contribution of the green-K353 pattern is very similar in the two strains (-17.8~pm ~0.2~kcal/mol and -22.7~pm ~0.1~kcal/mol for SARS-CoV-2 and SARS-CoV, respectively). Five groups of mutations draw our attention at the RBD–ACE2 binding interface, among them, the mutation –PPA469-471/GVEG482-485 has the most important and favorable impact on SARS-CoV-2 binding to the ACE2 receptor. These results, highlighting the molecular differences in the binding between the two viruses, contribute to the common knowledge about the new corona virus and to the development of appropriate antiviral treatments, addressing the necessity of ongoing pandemics.

Molecular dynamics simulation on the Thermosinus carboxydivorans pfl ZTP riboswitch by ligand binding

Riboswitches are RNA molecules that can regulate gene expression which is affected by ligand-binding during cotranscriptional folding process. However, the role of ligand during the folding is still unclear. In this study, the pfl domain of Thermosinus carboxydivorans ZTP riboswitch was discussed. The ligand is molecule ZMP. We mainly analyzed the change of ZMP-free and ZMP-bound aptamer domain by the dynamics simulation method. Structural features by calculating their RMSD, RMSF, etc. are analyzed. The results demonstrate that the binding domain require the presence of ZMP to maintain a stable fold. It also suggested that ZMP specificly binding to ZTP can generate more hydrogen bonds in the binding domain. Through the calculation of binding free energy decomposition of each nucleotide, molecule ZMP was found to promote the recognition and binding process of ligands by controlling some special nucleotides in the process of ligand binding. At last, the dynamical correlation and components of conformational motions were both applied to explore the effect of molecule ZMP to ZTP riboswitch. In general, ZMP can effectively affect the motions of the pfl riboswitch and facilitate the folding process of the ZTP riboswitch.These results may provide some new ideas for structural changes in riboswitches and their cotranscriptional folding process.

Studies on the antibacterial activities and molecular mechanism of GyrB inhibitors by 3D-QSAR, molecular docking and molecular dynamics simulation

DNA gyrase B (GyrB) plays a critical role in DNA replication, repair, recombination, and transcription and has become an attractive target for a number of antibacterial agents. In the present work, to better comprehend the structure–activity relationship and mechanism of action of novel series of GyrB inhibitors, a theoretical study of three-dimensional-quantitative structure–activity relationship (3D-QSAR), molecular docking, molecular dynamics (MD) simulations, and binding free energy analysis were performed. The results showed that CoMFA (R 2 cv = 0.591, R 2 pred = 0.7698) and CoMSIA (R 2 cv = 0.629, R 2 pred = 0.6848) models possessed robust stability and predictability. The derived contour maps of steric, electrostatic, and hydrogen bond donor field further displayed the modified information of these inhibitors. Molecular docking was further conducted to provide the initial binding conformation for MD simulations. Subsequently, MD simulations were applied to explore the key amino acids and binding modes at the active site. The binding free energy decomposition analysis further indicates that the residues Ile54, Glu55, Arg83, Ala85, Val86 and Thr128 are essential for the high selectivity of inhibitors. Overall, these results would serve as a significant guideline for the discovery and design of novel GyrB inhibitors.

In silico exploration of disulfide derivatives of Ferula foetida oleo-gum (Covexir®) as promising therapeutics against SARS-CoV-2

Although vaccines have been significantly successful against coronavirus, due to the high rate of the Omicron variant spread many researchers are trying to find efficient drugs against COVID-19. Herein, we conducted a computational study to investigate the binding mechanism of four potential inhibitors (including disulfide derivatives isolated from Ferula foetida) to SARS-CoV-2 main protease. Our findings revealed that the disulfides mainly interacted with HIS41, MET49, CYS145, HIS64, MET165, and GLN189 residues of SARS-CoV-2 main protease. The binding free energy decomposition results also showed that the van der Waals (vdW) energy plays the main role in the interaction of HIS41, MET49, CYS145, HIS64, MET165, and GLN189 residues with the inhibitors. Furthermore, it is found that the Z-isomer derivatives have a stronger interaction with SARS-CoV-2, and the strongest interaction belongs to the (Z)-1-(1-(methylthio)propyl)-2-(prop-1-enyl)disulfane (ΔG = −18.672 kcal/mol). The quantum mechanical calculations demonstrated that the second-order perturbation stabilization energy and the electron density values for MET49-ligand interactions are higher than the other residue-ligand complexes. This finding confirms the stronger interaction of this residue with the ligands.

Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix).

Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.g. a hydroxyl map for the ethanol) are largely influenced by the rest of the solvent molecule and do not reflect the intrinsic affinity of the moiety in question. As such, they are hardly transferable to different molecules. To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions. We demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential. Atomic decomposition of binding free energies derived from MDmix simulations provides transferable and quantitative binding free energy maps.

Protein-protein interaction of RdRp with its co-factor NSP8 and NSP7 to decipher the interface hotspot residues for drug targeting: A comparison between SARS-CoV-2 and SARS-CoV

In this study we explored the molecular mechanism of RdRp (Non-Structural Protein, NSP12) interaction with its co-factors NSP7 and NSP8 which is the main toolbox for RNA replication and transcription of SARS-CoV-2 and SARS-CoV. The replication complex is a heterotetramer consists of one NSP12, one NSP7 and two NSP8. Extensive molecular dynamics (MD) simulations were applied on both the heterotetramer complexes to generate the conformations and were used to estimate the MMPBSA binding free energy (BFE) and per-residue energy decomposition of NSP12-NSP8 and NSP12-NSP7 and NSP7-NSP8 complexes. The BFE of SARS-CoV-2 heterotetramer complex with its corresponding partner protein was significantly higher as compared to SARS-CoV. Interface hotspot residues were predicted using different methods implemented in KFC (Knowledge-based FADA and Contracts), HotRegion and Robetta web servers. Per-residue energy decomposition analysis showed that the predicted interface hotspot residues contribute more energy towards the formation of complexes and most of the predicted hotspot residues are clustered together. However, there is a slight difference in the residue-wise energy contribution in the interface NSPs on heterotetramer viral replication complex of both coronaviruses. While the overall replication complex of SARS-CoV-2 was found to be slightly flexible as compared to SARS-CoV. This difference in terms of structural flexibility/stability and energetic characteristics of interface residues including hotspots at PPI interface in the viral replication complexes may be the reason of higher rate of RNA replication of SARS-CoV-2 as compared to SARS-CoV. Overall, the interaction profile at PPI interface such as, interface area, hotspot residues, nature of bonds and energies between NSPs, may provide valuable insights in designing of small molecules or peptide/peptidomimetic ligands which can fit into the PPI interface to disrupt the interaction.

Molecular Modeling and Molecular Dynamics Simulation Studies on the Selective Binding Mechanism of MTHFD2 Inhibitors

Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is a mitochondrial enzyme that plays an important role in purinecarbon metabolism and thymidine biosynthesis. It has attracted broad interest as a novel therapeutic target for cancer. However, a major problem of current MTHFD2 inhibitors is their lack of selectivity and reactivity with its closest isoform, MTHFD1. Recently, the first selective MTHFD2 inhibitor, DS44960156, has been reported and it exhibits a more than 18-fold selectivity for MTHFD2 over MTHFD1. However, mechanism of DS44960156 selective binding to MTHFD2 over MTHFD1 is unknown. In this study, molecular docking, molecular dynamics (MD) simulations, molecular mechanics generalized born/surface area (MM_GBSA) binding free energy calculations, and analysis of the decomposition of binding free energies were used to investigate the selective binding mechanism of DS44960156 to the folate-binding site of MTHFD2 over MTHFD1. The results revealed that contributions from residues Gln100/Gln132, Val55/Asn87, and Gly237/Gly310 in the binding pocket of MTHFD1/MTHFD2 are the key factors responsible for the binding selectivity. These findings explain the selectivity of DS44960156 to MTHFD2 over MTHFD1, and may provide guidance for the future study and design of novel MTHFD2 inhibitors.

Molecular mechanism of anionic stabilizer for telomere G-quadruplex.

Telomere DNA assumes a high-order G-quadruplex (G4) structure, stabilization of which prevents telomere lengthening by telomerase in cancer. Through applying combined molecular simulation methods, an investigation on the selective binding mechanism of anionic phthalocyanine 3,4',4'',4'''-tetrasulfonic acid (APC) and human hybrid (3 + 1) G4s was firstly performed at the atomic level. Compared to the groove binding mode of APC and the hybrid type I (hybrid-I) telomere G4, APC preferred to bind to the hybrid type II (hybrid-II) telomere G4 via end-stacking interactions, which showed much more favorable binding free energies. Analyses of the non-covalent interaction and binding free energy decomposition revealed a decisive role of van der Waals interaction in the binding of APC and telomere hybrid G4s. And the binding of APC and hybrid-II G4 that showed the highest binding affinity adopted the end-stacking binding mode to form the most extensive van der Waals interactions. These findings add new knowledge to the design of selective stabilizers targeting telomere G4 in cancer.

3D-QSAR, Molecular Docking, and MD Simulations of Anthraquinone Derivatives as PGAM1 Inhibitors.

PGAM1 is overexpressed in a wide range of cancers, thereby promoting cancer cell proliferation and tumor growth, so it is gradually becoming an attractive target. Recently, a series of inhibitors with various structures targeting PGAM1 have been reported, particularly anthraquinone derivatives. In present study, the structure–activity relationships and binding mode of a series of anthraquinone derivatives were probed using three-dimensional quantitative structure–activity relationships (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, r2 = 0.97, q2 = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2 = 0.96, q2 = 0.82) techniques were performed to produce 3D-QSAR models, which demonstrated satisfactory results, especially for the good predictive abilities. In addition, molecular dynamics (MD) simulations technology was employed to understand the key residues and the dominated interaction between PGAM1 and inhibitors. The decomposition of binding free energy indicated that the residues of F22, K100, V112, W115, and R116 play a vital role during the ligand binding process. The hydrogen bond analysis showed that R90, W115, and R116 form stable hydrogen bonds with PGAM1 inhibitors. Based on the above results, 7 anthraquinone compounds were designed and exhibited the expected predictive activity. The study explored the structure–activity relationships of anthraquinone compounds through 3D-QSAR and molecular dynamics simulations and provided theoretical guidance for the rational design of new anthraquinone derivatives as PGAM1 inhibitors.