Assessing the Performance of MM/PBSA, MM/GBSA, and QM-MM/GBSA Approaches on Protein/Carbohydrate Complexes: Effect of Implicit Solvent Models, QM Methods, and Entropic Contributions

From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor whose ligand-induced conformational changes, primarily driven by helix 12 (H12) repositioning, regulate transcriptional activity. However, the precise mechanism remains elusive. In this study, we performed classical molecular dynamics (cMD) simulations of the PPARγ ligand binding domain (LBD) in complex with two agonists (BRL, 3EA), a partial agonist (GW0072), and an antagonist (EKP), generating 3 μs trajectories for each system. To gain deeper insights, we integrated machine learning-assisted clustering with MD simulations, revealing a favorable trend in binding free energy (ΔG b), suggesting enhanced complex stability. A case study on EKP demonstrated that, despite fitting within the binding site, it failed to induce rapid LBD or H12 rearrangements in the apo agonist-induced conformation. Additionally, we investigated the apo-state conformations of PPARγ-LBD influenced by agonist and antagonist ligands, utilizing cMD and Gaussian accelerated molecular dynamics (GaMD) over a cumulative 6 μs (3 μs cMD + 3 μs GaMD). Key residues known to modulate PPARγ function upon mutation were analyzed, and simulations confirmed the high stability of both apo and ligand-bound conformations. Notably, in the apo state, specific H12 residues interacted with other PPARγ-LBD regions, preventing disorder and abrupt transitions. These findings guided the selection of collective variables (CVs) for well-tempered metadynamics (WT-MetaD) simulations, which-in the apo-agonist state-captured the H12 shift from agonist- to antagonist-like conformations, consistent with resolved X-ray structures. Overall, this computational framework provides novel insights into PPARγ-LBD conformational dynamics and establishes a valuable approach for rationally assessing the effects of modulators on PPARγ activity.

Mechanistic and structural insights into EstS1 esterase: A potent broad-spectrum phthalate diester degrading enzyme

Phthalate diesters are important pollutants and act as endocrine disruptors. While certain bacterial esterases have been identified for phthalate diesters degradation to monoesters, their structural and mechanistic characteristics remain largely unexplored. Here, we highlight the potential of the thermostable and pH-tolerant EstS1 esterase from Sulfobacillus acidophilus DSM10332 to degrade high molecular weight bis(2-ethylhexyl) phthalate (DEHP) by combining biophysical and biochemical approaches along with high-resolution EstS1 crystal structures of the apo form and with bound substrates, products, and their analogs to elucidate its mechanism. The catalytic tunnel mediates entry and exit of the substrate and product, respectively. The centralized Ser-His-Asp triad performs catalysis by a bi-bi ping-pong mechanism, forming a tetrahedral intermediate. Mutagenesis analysis showed that the Met207Ala mutation abolished DEHP binding at the active site, confirming its essential role in supporting catalysis. These findings underscore EstS1 as a promising tool for advancing technologies aimed at phthalate diesters biodegradation.

Computational insights in repurposing a cardiovascular drug for Alzheimer's disease: the role of aromatic amino acids in stabilizing the drug through π-π stacking interaction

Alzheimer's disease (AD) is a neurological condition that worsens over time and causes linguistic difficulties, cognitive decline, and memory loss. Since AD is a complicated, multifaceted illness, it is critical to identify drugs to combat this degenerative condition. Histone deacetylase 2 (HDAC2) represents a promising epigenetic target for neurodegenerative diseases. So, for this study, we chose HDAC2 as the targeted protein. Repurposing drugs has many advantages, including reduced costs and high profits. There is a lower probability of malfunction because the unique drug candidate has previously completed numerous investigations. In this study, we have taken 58 clinically approved food and drug administration (FDA) drugs utilized in clinical trials for AD. Molecular docking was carried out for the 58 compounds. The telmisartan drug has the highest binding score of -9.4 kcal mol-1. The angiotensin II receptor blocker (ARB) telmisartan has demonstrated some promise in AD research as of the last update in January 2022. However, its exact significance in treating or preventing AD is still being studied. Molecular dynamics (MD) and molecular mechanics with generalized born and surface area solvation (MM-GBSA)/interaction entropy (IE) calculations were carried out to study the structural stability of the complexes. Umbrella sampling (US) techniques are a cutting-edge drug development method to understand more about the interactions between protein and ligand. π-π stacking interactions play a major role in helping the ligand to bind in the zinc bounding domain of the protein. From these analyses, we conclude that telmisartan, which is a cardiovascular drug, is more potent than the other drugs to treat AD. The anti-inflammatory, neuroprotective, and blood-brain barrier-crossing qualities of telmisartan make it a promising therapeutic agent for AD; however, more research, including larger clinical trials, is needed to determine the drug's precise role in treating AD.

Comparative Assessment of Water Models in Protein-Glycan Interaction: Insights from Alchemical Free Energy Calculations and Molecular Dynamics Simulations

Accurate computational simulations of protein-glycan dynamics are crucial for a comprehensive understanding of critical biological mechanisms, including host-pathogen interactions, immune system defenses, and intercellular communication. The accuracy of these simulations, including molecular dynamics (MD) simulation and alchemical free energy calculations, critically relies on the appropriate parameters, including the water model, because of the extensive hydrogen bonding with glycan hydroxyl groups. However, a systematic evaluation of water models' accuracy in simulating protein-glycan interaction at the molecular level is still lacking. In this study, we used full atomistic MD simulations and alchemical absolute binding free energy (ABFE) calculations to investigate the performance of five distinct water models in six protein-glycan complex systems. We evaluated water models' impact on structural dynamics and binding affinity through over 5.8 μs of simulation time per system. Our results reveal that most protein-glycan complexes are stable in the overall structural dynamics regardless of the water model used, while some show obvious fluctuations with specific water models. More importantly, we discover that the stability of the binding motif's conformation is dependent on the water model chosen when its residues form weak hydrogen bonds with the glycan. The water model also influences the conformational stability of the glycan in its bound state according to density functional theory (DFT) calculations. Using alchemical ABFE calculations, we find that the OPC water model exhibits exceptional consistency with experimental binding affinity data, whereas commonly used models such as TIP3P are less accurate. The findings demonstrate how different water models affect protein-glycan interactions and the accuracy of binding affinity calculations, which is crucial in developing therapeutic strategies targeting these interactions.

Computational Insights for Interactions between nsP2 and nsP3 of CHIKV and Hormones through DFT Computations and Molecular Dynamics Simulations

The non-structural protein (nsP2 & nsP3) of the Chikungunya virus (CHIKV) is responsible for the transmission of viral infection. The main role of non-structural proteins are involved in the transcription process at an early stage of the infection. In this work, authors have studied the impact of nsP2 and nsP3 of CHIKV on hormones present in the human body using a computational approach. The ten hormones of chemical properties such as 4-Androsterone-2,17-dione, aldosterone, androsterone, corticosterone, cortisol, cortisone, estradiol, estrone, progesterone and testosterone were taken as a potency. From the molecular docking, the binding energy of the complexes is estimated, and cortisone was found to be the highest negative binding energy (-6.57 kcal/mol) with the nsP2 and corticosterone with the nsP3 (-6.47 kcal/mol). This is based on the interactions between hormones and nsP2/nsP3, which are types of noncovalent intermolecular interactions categorized into three types: electrostatic interactions, van der Waals (vdW) interactions, and hydrogen-bonding (H-bonding) interactions. To validate the docking results, additional molecular dynamics simulations and MM-GBSA methods were performed. The change in enthalpy, entropy, and free energy were calculated using MM-GBSA methods. The nsP2 and nsP3 of CHIKV interact strongly with the cortisone and corticosterone with free energy changes of -20.55 & -36.08 kcal/mol, respectively. Methods: The crystal structures of 3TKR and 3GPO proteins of nsP2 and nsP3 were extracted from the RCSB Protein Data Bank. Initially, unnecessary atoms like extra cations or anions and missing explicit hydrogen atoms were removed and added from the native domain of nsP2 and nsP3. The alignment of coordinated in the native domain was performed using Chimera and Notepad++ tools. The molecular docking of protein and ligand was performed usingAutoDock tool; it is essential for the prediction of the orientation of the ligand into the cavity of the target protein based on binding affinity. Based on thermodynamic parameters, MD Simulations were employed to calculate the change in binding free energies of various complexes followed by a change in enthalpy and entropy with time. According to MD production, the CPPTAJ and PTRAJ programs were used to analyse the trajectories, such as dynamic stability (RMSD), residual fluctuation (RMSF), compatibility, and hydrogen bonds of the newly formed complexes. After that, the Density Functional Theory (DFT) were used to calculate the electronic properties of selected molecules by Gaussian 16 on applying the B3LYP method with the 6-311G (d, p) basis set.

New insight into synergistic interaction between the backbone or side chain of xanthan gum and the backbone of Gleditsia sinensis polysaccharide

In this study, the synergistic effect and weak gel mechanism of XG and Gleditsia sinensis polysaccharide (GSP) in different ratios were studied through the rheological properties, microstructure and molecular simulation based on density functional theory (DFT). The results of rheological properties showed that the mixtures formed a weak gel at the concentration of 0.5 % (w/v), with the synergistic impact peaking at a XG/GSP ratio of 3:7. Weak gels produced by XG and GSP had the intersection of G' and G" within the temperature sweep range, and the largest change in the G' slope at a XG/GSP ratio of 3:7. By calculating the interaction energy, it was found that the backbone of XG was more likely to interact with the backbone of GSP. Furthermore, the XG mainchain intersected with the backbone of GSP in a cross shape ("X" shape). As a result, this paper proposed a possible mechanism for the formation of the XG/GSP weak gel, with XG as the main chain and GSP as the grid point, and the main interaction type being hydrogen bonding, with the van der Waals force also involved. The results provide new insight for designing and producing physical gels with specific interactions in food industry.

Computational and experimental analysis of Luteolin-β-cyclodextrin supramolecular complexes: Insights into conformational dynamics and phase solubility

Investigating the structural stability of poorly-soluble luteolin (LuT) after encapsulation within cyclodextrins (CDs) is crucial for unlocking the therapeutic potential of LuT bioactive molecule. Herein, native and modified β-CD were employed to investigate LuT inclusion complex formation. Molecular mechanics (MM) and quantum mechanics (QM) were utilized for structural dynamics analysis. Microsecond timescale MD simulations yielded insights into LuT-CD interactions. The binding affinity between LuT and selected β-CDs was assessed by calculating the binding free energy using MM-PBSA and umbrella sampling simulations. The MM-PBSA results indicated that Heptakis-O-(2-hydroxypropyl)-β-CD (HP-β-CD) (-82.59+/-11.67 kJ/mol) and Di-O-methyl-β-CD (DM-β-CD) (-54.01+/-11.07 kJ/mol) exhibited good binding affinity for LuT. Subsequently, derivative screening of HP-β-CD revealed that only 2-HP-β-CD (HP-β-CD-1)/LuT (-21.38 kJ/mol) displayed a superior binding free energy (obtained from umbrella sampling) than HP-β-CD/LuT (-19.15 kJ/mol) inclusion complex. We conducted QM calculations on the top three complexes namelly HP-β-CD, DM-β-CD, and HP-β-CD-1 employing wB97X-D/6-311 + G(d,p) model chemistry to strengthen the MM results. The computational analysis aligns with experimental findings (phase solubility analysis), validating HP-β-CD-1 as most effective cavitand molecule for improving the solubility of LuT. This study offers critical structural insights for developing novel HP-β-CD derivatives with enhanced host capacity to encapsulate guest molecules efficiently.

Novel Autotaxin Inhibitor ATX-1d Significantly Enhances Potency of Paclitaxel-An In Silico and In Vitro Study

The development of drug resistance in cancer cells poses a significant challenge for treatment, with nearly 90% of cancer-related deaths attributed to it. Over 50% of ovarian cancer patients and 30-40% of breast cancer patients exhibit resistance to therapies such as Taxol. Previous literature has shown that cytotoxic cancer therapies and ionizing radiation damage tumors, prompting cancer cells to exploit the autotaxin (ATX)-lysophosphatidic acid (LPA)-lysophosphatidic acid receptor (LPAR) signaling axis to enhance survival pathways, thus reducing treatment efficacy. Therefore, targeting this signaling axis has become a crucial strategy to overcome some forms of cancer resistance. Addressing this challenge, we identified and assessed ATX-1d, a novel compound targeting ATX, through computational methods and in vitro assays. ATX-1d exhibited an IC50 of 1.8 ± 0.3 μM for ATX inhibition and demonstrated a significant binding affinity for ATX, as confirmed by MM-GBSA, QM/MM-GBSA, and SAPT in silico methods. ATX-1d significantly amplified the potency of paclitaxel, increasing its effectiveness tenfold in 4T1 murine breast carcinoma cells and fourfold in A375 human melanoma cells without inducing cytotoxic effects as a single agent.

Network pharmacology and in vitro experimental verification unveil glycyrrhizin from glycyrrhiza glabra alleviates acute pancreatitis via modulation of MAPK and STAT3 signaling pathways

Acute pancreatitis (AP) is a severe gastrointestinal inflammatory disease with increasing mortality and morbidity. Glycyrrhiza glabra, commonly known as Liquorice, is a widely used plant containing bioactive compounds like Glycyrrhizin, which possesses diverse medicinal properties such as anti-inflammatory, antioxidant, antiviral, and anticancer activities. The objective of this study is to investigate the active components, relevant targets, and underlying mechanisms of the traditional Chinese medicine Glycyrrhiza glabra in the treatment of AP. Utilizing various computational biology methods, we explored the potential targets and molecular mechanisms through Glycyrrhizin supplementation. Computational results indicated that Glycyrrhizin shows promising pharmacological potential, particularly with mitogen-activated protein kinase 3 (MAPK3) protein (degree: 70), forming stable complexes with Glycyrrhizin through ionic and hydrogen bonding interactions, with a binding free energy (ΔGbind) of -33.01 ± 0.08 kcal/mol. Through in vitro experiments, we validated that Glycyrrhizin improves primary pancreatic acinar cell injury by inhibiting the MAPK/STAT3/AKT signaling pathway. Overall, MAPK3 emerges as a reliable target for Glycyrrhizin's therapeutic effects in AP treatment. This study provides novel insights into the active components and potential targets and molecular mechanisms of natural products.

Structural plasticity of omicron BA.5 and BA.2.75 for enhanced ACE-dependent entry into cells

The current study investigated the binding variations among the wilt type, Omicron sub-variants BA.2.75 and BA.5, using protein-protein docking, protein structural graphs (P SG), and molecular simulation methods. HADDOCK predicted docking scores and dissociation constant (KD) revealed tighter binding of these sub-variants in contrast to the WT. Further investigation revealed variations in the hub residues, protein sub-networks, and GlobalMetapath in these variants as compared to the WT. A very unusual dynamic for BA.2.75 and BA.5 was observed, and secondary structure transition can also be witnessed in the loops (44-505). The results show that the flexibility of these three loops is increased by the mutations as an allosteric effect and thus enhances the chances of bonding with the nearby residues to connect and form a stable connection. Furthermore, the additional hydrogen bonding contacts steer the robust binding of these variants in contrast to the wild type. The total binding free energy for the wild type was calculated to be -61.38 kcal/mol, while for BA.2.75 and BA.5 variants the T BE was calculated to be -70.42 kcal/mol and 69.78 kcal/mol, respectively. We observed that the binding of BA.2.75 is steered by the electrostatic interactions while the BA.5 additional contacts are due to the vdW (Van der Waal) energy. From these findings, it can be observed the Spike (S) protein is undergoing structural adjustments to bind efficiently to the hACE2 (human angiotensin-converting enzyme 2) receptor and, in turn, increase entry to the host cells. The current study will aid the development of structure-based drugs against these variants.Communicated by Ramaswamy H. Sarma.

Structure-yeast α-glucosidase inhibitory activity relationship of 9-O-berberrubine carboxylates

Thirty-five 9-O-berberrubine carboxylate derivatives were synthesized and evaluated for yeast α-glucosidase inhibitory activity. All compounds demonstrated better inhibitory activities than the parent compounds berberine (BBR) and berberrubine (BBRB), and a positive control, acarbose. The structure-activity correlation study indicated that most of the substituents on the benzoate moiety such as methoxy, hydroxy, methylenedioxy, benzyloxy, halogen, trifluoromethyl, nitro and alkyl can contribute to the activities except multi-methoxy, fluoro and cyano. In addition, replacing benzoate with naphthoate, cinnamate, piperate or diphenylacetate also led to an increase in inhibitory activities except with phenyl acetate. 9, 26, 27, 28 and 33 exhibited the most potent α-glucosidase inhibitory activities with the IC50 values in the range of 1.61-2.67 μM. Kinetic study revealed that 9, 26, 28 and 33 interacted with the enzyme via competitive mode. These four compounds were also proved to be not cytotoxic at their IC50 values. The competitive inhibition mechanism of these four compounds against yeast α-glucosidase was investigated using molecular docking and molecular dynamics simulations. The binding free energy calculations suggest that 26 exhibited the strongest binding affinity, and its binding stability is supported by hydrophobic interactions with D68, F157, F158 and F177. Therefore, 9, 26, 28 and 33 would be promising candidates for further studies of antidiabetic activity.

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.

Targeting SARS-CoV-2 Macrodomain-1 to Restore the Innate Immune Response Using In Silico Screening of Medicinal Compounds and Free Energy Calculation Approaches

Among the different drug targets of SARS-CoV-2, a multi-domain protein known as NSP3 is a critical element of the translational and replication machinery. The macrodomain-I, in particular, has been reported to have an essential role in the viral attack on the innate immune response. In this study, we explore natural medicinal compounds and identify potential inhibitors to target the SARS-CoV-2-NSP3 macrodomain-I. Computational modeling and simulation tools were utilized to investigate the structural-dynamic properties using triplicates of 100 ns MD simulations. In addition, the MM/GBSA method was used to calculate the total binding free energy of each inhibitor bound to macrodomain-I. Two significant hits were identified: 3,5,7,4'-tetrahydroxyflavanone 3'-(4-hydroxybenzoic acid) and 2-hydroxy-3-O-beta-glucopyranosyl-benzoic acid. The structural-dynamic investigation of both compounds with macrodomain-I revealed stable dynamics and compact behavior. In addition, the total binding free energy for each complex demonstrated a robust binding affinity, of ΔG -61.98 ± 0.9 kcal/mol for Compound A, while for Compound B, the ΔG was -45.125 ± 2.8 kcal/mol, indicating the inhibitory potential of these compounds. In silico bioactivity and dissociation constant (KD) determination for both complexes further validated the inhibitory potency of each compound. In conclusion, the aforementioned natural products have the potential to inhibit NSP3, to directly rescue the host immune response. The current study provides the basis for novel drug development against SARS-CoV-2 and its variants.

Docking and Molecular Dynamics Simulations Clarify Binding Sites for Interactions of Novel Marine Sulfated Glycans with SARS-CoV-2 Spike Glycoprotein

The entry of SARS-CoV-2 into the host cell is mediated by its S-glycoprotein (SGP). Sulfated glycans bind to the SGP receptor-binding domain (RBD), which forms a ternary complex with its receptor angiotensin converting enzyme 2. Here, we have conducted a thorough and systematic computational study of the binding of four oligosaccharide building blocks from novel marine sulfated glycans (isolated from Pentacta pygmaea and Isostichopus badionotus) to the non-glycosylated and glycosylated RBD. Blind docking studies using three docking programs identified five potential cryptic binding sites. Extensive site-targeted docking and molecular dynamics simulations using two force fields confirmed only two binding sites (Sites 1 and 5) for these novel, highly charged sulfated glycans, which were also confirmed by previously published reports. This work showed the structural features and key interactions driving ligand binding. A previous study predicted Site 2 to be a potential binding site, which was not observed here. The use of several molecular modeling approaches gave a comprehensive assessment. The detailed comparative study utilizing multiple modeling approaches is the first of its kind for novel glycan-SGP interaction characterization. This study provided insights into the key structural features of these novel glycans as they are considered for development as potential therapeutics.

Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED's ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC50 value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

Designing Potent α-Glucosidase Inhibitors: A Synthesis and QSAR Modeling Approach for Biscoumarin Derivatives

Nineteen biscoumarins were synthesized, well-characterized, and evaluated against α-glucosidases in vitro. Of these, six compounds (10, 12, 16, and 17-19) were newly synthesized and not previously reported in the chemical literature. The majority of the synthesized derivatives demonstrated significant inhibitory activity. A quantitative structure-activity relationship (QSAR) model was developed, revealing a strong correlation between the anti-α-glucosidase activity and selected molecular descriptors. Based on this model, two new compounds (18 and 19) were designed, which exhibited the strongest inhibition with IC₅₀ values of 0.62 and 1.21 μM, respectively, when compared to the positive control (acarbose) with an IC₅₀ value of 93.63 μM. Enzyme kinetic studies of compounds 18 and 19 revealed their competitive inhibition with K_i values of 3.93 and 1.80 μM, respectively. Computational studies demonstrated that compound 18 could be inserted into the original binding site (OBS) of α-glucosidase MAL12 and form multiple hydrophobic interactions with nearby amino acids, with the bromo group playing an essential role in enhancing the binding strength and stability at the OBS of the enzyme based on the quantum mechanical calculations using the fragment molecular orbital method. These findings provide valuable insights into the design of potent α-glucosidase inhibitors, which may have potential therapeutic applications in the treatment of diabetes and related diseases.

Exploring the therapeutic mechanisms of Gleditsiae Spina acting on pancreatic cancer via network pharmacology, molecular docking and molecular dynamics simulation

Pancreatic cancer is one of the most aggressive tumors and also has a low survival rate. The dried spines of Gleditsia sinensis Lam are known as "Gleditsiae Spina" and they mostly contain flavonoids, phenolic acids, terpenoids, steroids, and other chemical components. In this study, the potential active components and molecular mechanisms of Gleditsiae Spina for treating pancreatic cancer were systematically revealed by network pharmacology, molecular docking and molecular dynamics simulations (MDs). RAC-alpha serine/threonine-protein kinase (AKT1), cellular tumor antigen p53 (TP53), tumor necrosis factor α (TNFα), interleukin-6 (IL6) and vascular endothelial growth factor A (VEGFA) were common targets of Gleditsiae Spina, human cytomegalovirus infection signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and MAPK signaling pathway were critical pathways of fisetin, eriodyctiol, kaempferol and quercetin in the treatment of pancreatic cancer. Molecular dynamics simulations (MDs) results showed that eriodyctiol and kaempferol have long-term stable hydrogen bonds and high binding free energy for TP53 (-23.64 ± 0.03 kcal mol-1 and -30.54 ± 0.02 kcal mol-1, respectively). Collectively, our findings identify active components and potential targets in Gleditsiae Spina for the treatment of pancreatic cancer, which may help to explore leading compounds and potential drugs for pancreatic cancer.

Structure-based approach: molecular insight of pyranocumarins against α-glucosidase through computational studies

α-glucosidase is an enzyme that catalyzes the release of α-glucose molecules through hydrolysis reactions. Regulation of this enzyme can increase sugar levels in type-2 diabetes mellitus (DM) patients. Pyranocoumarin derivatives have been identified as α-glucosidase inhibitors. Through an in silico approach, this work studied the inhibition of three pyranocoumarin compounds against the α-glucosidase at the molecular level. Molecular docking and molecular dynamics simulation were performed to understand the dynamics behavior of pyranocoumarin derivatives against α-glucosidase. The prediction of free binding energy (ΔG _bind) using the Quantum Mechanics/Molecular Mechanics-Generalized Born (QM/MM-GBSA) approach for each system had the following results, PC1-α-Glu: -13.97 kcal mol^-1, PC2-α-Glu: -3.69 kcal mol^-1, and PC3-α-Glu: -13.68 kcal mol^-1. The interaction energy of each system shows that the grid score, ΔG _bind, and ΔG _exp values had a similar correlation, that was PC1-α-Glu > PC3-α-Glu > PC2-α-Glu. Additionally, the decomposition energy analysis (ΔG ^residue _bind) was carried out to find out the contribution of the key binding residue. The results showed that there were 15 key binding residues responsible for stabilizing pyranocumarin binding with criteria of ΔG ^residue _bind < -1.00 kcal mol^-1. The evaluation presented in this work could provide information on the molecular level about the inhibitory efficiency of pyranocoumarin derivatives against a-glucosidase enzyme based on computational studies.

Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems

Hybrid quantum mechanics/molecular mechanics (QM/MM) is one of the most reliable approaches for accurately modeling and studying the complex pharmaceutical discovery system. Classical mechanics has significantly accelerated the drug discovery process in the past decade. However, the current challenge is the large pool of false positives, which require extensive validation. Hybrid QM/MM is an effective solution for accurately studying ligand binding, structural mechanisms, free energy evaluation, and spectroscopic characterization. This article highlights the methodological details relevant to cost-effective hybrid QM/MM methods. This approach, combined with traditional pharmacoinformatics methods, could be a reliable strategy to balance the cost and accuracy of the calculations.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Studies on the Selectivity Mechanism of Wild-Type E. coli Thioesterase ‘TesA and Its Mutants for Medium- and Long-Chain Acyl Substrates

E. coli thioesterase ‘TesA is an important enzyme in fatty acid production. Medium-chain fatty acids (MCFAs, C6-C10) are of great interest due to their similar physicochemical properties to petroleum-based oleo-chemicals. It has been shown that wild-type ‘TesA had better selectivity for long-chain acyl substrates (≥C16), while the two mutants ‘TesAE142D/Y145G and ‘TesAM141L/E142D/Y145G had better selectivity for medium-chain acyl substrates. However, it is difficult to obtain the selectivity mechanism of substrates for proteins by traditional experimental methods. In this study, in order to obtain more MCFAs, we analyzed the binding mode of proteins (‘TesA, ‘TesAE142D/Y145G and ‘TesAM141L/E142D/Y145G) and substrates (C16/C8-N-acetylcysteamine analogs, C16/C8-SNAC), the key residues and catalytic mechanisms through molecular docking, molecular dynamics simulations and the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA). The results showed that several main residues related to catalysis, including Ser10, Asn73 and His157, had a strong hydrogen bond interaction with the substrates. The mutant region (Met141-Tyr146) and loop107–113 were mainly dominated by Van der Waals contributions to the substrates. For C16-SNAC, except for ‘TesAM141L/E142D/Y145G with large conformational changes, there were strong interactions at both head and tail ends that distorted the substrate into a more favorable high-energy conformation for the catalytic reaction. For C8-SNAC, the head and tail found it difficult to bind to the enzyme at the same time due to insufficient chain length, which made the substrate binding sites more variable, so ‘TesAM141L/E142D/Y145G with better binding sites had the strongest activity, and ‘TesA had the weakest activity, conversely. In short, the matching substrate chain and binding pocket length are the key factors affecting selectivity. This will be helpful for the further improvement of thioesterases.

MDO: A Computational Protocol for Prediction of Flexible Enzyme-Ligand Binding Mode

AIM

Developing a method for use in computer aided drug design Background: Predicting the structure of enzyme-ligand binding mode is essential for understanding the properties, functions, and mechanisms of the bio-complex, but is rather difficult due to the enormous sampling space involved.

OBJECTIVE

Accurate prediction of enzyme-ligand binding mode conformation.

METHOD

A new computational protocol, MDO, is proposed for finding the structure of ligand binding pose. MDO consists of sampling enzyme sidechain conformations via molecular dynamics simulation of enzyme-ligand system and clustering of the enzyme configurations, sampling ligand binding poses via molecular docking and clustering of the ligand conformations, and the optimal ligand binding pose prediction via geometry optimization and ranking by the ONIOM method. MDO is tested on 15 enzyme-ligand complexes with known accurate structures.

RESULTS

The success rate of MDO predictions, with RMSD < 2 Å, is 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities.

CONCLUSION

The MDO protocol provides high quality enzyme-ligand binding mode prediction with reasonable computational cost. The MDO protocol is recommended for use in the structure-based drug design.

Discovery of a lectin domain that regulates enzyme activity in mouse N-acetylglucosaminyltransferase-IVa (MGAT4A)

N-Glycosylation is a common post-translational modification, and the number of GlcNAc branches in N-glycans impacts glycoprotein functions. N-Acetylglucosaminyltransferase-IVa (GnT-IVa, also designated as MGAT4A) forms a β1-4 GlcNAc branch on the α1-3 mannose arm in N-glycans. Downregulation or loss of GnT-IVa causes diabetic phenotypes by dysregulating glucose transporter-2 in pancreatic β-cells. Despite the physiological importance of GnT-IVa, its structure and catalytic mechanism are poorly understood. Here, we identify the lectin domain in mouse GnT-IVa's C-terminal region. The crystal structure of the lectin domain shows structural similarity to a bacterial GlcNAc-binding lectin. Comprehensive glycan binding assay using 157 glycans and solution NMR reveal that the GnT-IVa lectin domain selectively interacts with the product N-glycans having a β1-4 GlcNAc branch. Point mutation of the residue critical to sugar recognition impairs the enzymatic activity, suggesting that the lectin domain is a regulatory subunit for efficient catalytic reaction. Our findings provide insights into how branching structures of N-glycans are biosynthesized.

Comparative assessment of QM-based and MM-based models for prediction of protein-ligand binding affinity trends

Methods which accurately predict protein-ligand binding strengths are critical for drug discovery. In the last two decades, advances in chemical modelling have enabled steadily accelerating progress in the discovery and optimization of structure-based drug design. Most computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding. In this study, we show the competitiveness of our QM-based Molecules-in-Molecules (MIM) fragmentation method for characterizing binding energy trends for seven different datasets of protein-ligand complexes. By using molecular fragmentation, the MIM method allows for accelerated QM calculations. We demonstrate that for classes of structurally similar ligands bound to a common receptor, MIM provides excellent correlation to experiment, surpassing the more popular Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) methods. The MIM method offers a relatively simple, well-defined protocol by which binding trends can be ascertained at the QM level and is suggested as a promising option for lead optimization in structure-based drug design.

Comparative Structural Dynamics of Isoforms of Helicobacter pylori Adhesin BabA Bound to Lewis b Hexasaccharide via Multiple Replica Molecular Dynamics Simulations

BabA of Helicobacter pylori is the ABO blood group antigen-binding adhesin. Despite considerable diversity in the BabA sequence, it shows an extraordinary adaptation in attachment to mucosal layers. In the current study, multiple replica molecular dynamics simulations were conducted in a neutral aqueous solution to elucidate the conformational landscape of isoforms of BabA bound to Lewis b (Le^b) hexasaccharide. In addition, we also investigated the underlying molecular mechanism of the BabA-glycan complexation using the MM/GBSA scheme. The conformational dynamics of Le^b in the free and protein-bound states were also studied. The carbohydrate-binding site across the four isoforms was examined, and the conformational variability of several vital loops was observed. The cysteine–cysteine loops and the two diversity loops (DL1 and DL2) were identified to play an essential role in recognizing the glycan molecule. The flexible crown region of BabA was stabilized after association with Le^b. The outward movement of the DL2 loop vanished upon ligand binding for the Spanish specialist strain (S381). Our study revealed that the S831 strain shows a stronger affinity to Le^b than other strains due to an increased favorable intermolecular electrostatic contribution. Furthermore, we showed that the α1-2-linked fucose contributed most to the binding by forming several hydrogen bonds with key amino acids. Finally, we studied the effect of the acidic environment on the BabA-glycan complexation via constant pH MD simulations, which showed a reduction in the binding free energy in the acidic environment. Overall, our study provides a detailed understanding of the molecular mechanism of Le^b recognition by four isoforms of H. pylori that may help the development of therapeutics targeted at inhibiting H. pylori adherence to the gastric mucosa.

Crystal structure of Acetyl-CoA carboxylase (AccB) from Streptomyces antibioticus and insights into the substrate-binding through in silico mutagenesis and biophysical investigations

Acetyl-CoA carboxylase (ACC) is crucial for polyketides biosynthesis and acts as an essential metabolic checkpoint. It is also an attractive drug target against obesity, cancer, microbial infections, and diabetes. However, the lack of knowledge, particularly sequence-structure function relationship to narrate ligand-enzyme binding, has hindered the progress of ACC-specific therapeutics and unnatural "natural" polyketides. Structural characterization of such enzymes will boost the opportunity to understand the substrate binding, designing new inhibitors and information regarding the molecular rules which control the substrate specificity of ACCs. To understand the substrate specificity, we determined the crystal structure of AccB (Carboxyl-transferase, CT) from Streptomyces antibioticus with a resolution of 2.3 Å and molecular modeling approaches were employed to unveil the molecular mechanism of acetyl-CoA recognition and processing. The CT domain of S. antibioticus shares a similar structural organization with the previous structures and the two steps reaction was confirmed by enzymatic assay. Furthermore, to reveal the key hotspots required for the substrate recognition and processing, in silico mutagenesis validated only three key residues (V223, Q346, and Q514) that help in the fixation of the substrate. Moreover, we also presented atomic level knowledge on the mechanism of the substrate binding, which unveiled the terminal loop (500-514) function as an opening and closing switch and pushes the substrate inside the cavity for stable binding. A significant decline in the hydrogen bonding half-life was observed upon the alanine substitution. Consequently, the presented structural data highlighted the potential key interacting residues for substrate recognition and will also help to re-design ACCs active site for proficient substrate specificity to produce diverse polyketides.

Structure Based Affinity Maturation and Characterizing of SARS-CoV Antibody CR3022 against SARS-CoV-2 by Computational and Experimental Approaches

The COVID-19 epidemic is raging around the world. Neutralizing antibodies are powerful tools for the prevention and treatment of SARS-CoV-2 infection. Antibody CR3022, a SARS-CoV neutralizing antibody, was found to cross-react with SARS-CoV-2, but its affinity was lower than that of its binding with SARS-CoV, which greatly limited the further development of CR3022 against SARS-CoV-2. Therefore, it is necessary to improve its affinity to SARS-CoV-2 in vitro. In this study, the structure-based molecular simulations were utilized to virtually mutate the possible key residues in the complementarity-determining regions (CDRs) of the CR3022 antibody. According to the criteria of mutation energy, the mutation sites that have the potential to impact the antibody affinity were then selected. Then optimized CR3022 mutants with the enhanced affinity were further identified and verified by enzyme-linked immunosorbent assay (ELISA), surface plasma resonance (SPR) and autoimmune reactivity experiments. Finally, molecular dynamics (MD) simulation and binding free energy calculation (MM/PBSA) were performed on the wild-type CR3022 and its two double-site mutants to understand in more detail the contribution of these sites to the higher affinity. It was found that the binding affinity of the CR3022 antibody could be significantly enhanced more than ten times after the introduction of the S103F/Y mutation in HCDR–3 and the S33R mutation in LCDR–1. The additional hydrogen-bonding, hydrophobic interactions, as well as salt-bridges formed between the modified double-site mutated antibody and SARS-CoV-2 RBD were identified. The computational and experimental results clearly demonstrated that the affinity of the modified antibody has been greatly enhanced. This study indicates that CR3022 as a neutralizing antibody recognizing the conserved region of RBD against SARS-CoV with cross-reactivity with SARS-CoV-2, a different member in a large family of coronaviruses, could be improved by the computational and experimental approaches which provided insights for developing antibody drugs against SARS-CoV-2.

First-in-Class Inhibitors of the Ribosomal Oxygenase MINA53

MINA53 is a JmjC domain 2-oxoglutarate-dependent oxygenase that catalyzes ribosomal hydroxylation and is a target of the oncogenic transcription factor c-MYC. Despite its anticancer target potential, no small-molecule MINA53 inhibitors are reported. Using ribosomal substrate fragments, we developed mass spectrometry assays for MINA53 and the related oxygenase NO66. These assays enabled the identification of 2-(aryl)alkylthio-3,4-dihydro-4-oxoypyrimidine-5-carboxylic acids as potent MINA53 inhibitors, with selectivity over NO66 and other JmjC oxygenases. Crystallographic studies with the JmjC demethylase KDM5B revealed active site binding but without direct metal chelation; however, molecular modeling investigations indicated that the inhibitors bind to MINA53 by directly interacting with the iron cofactor. The MINA53 inhibitors manifest evidence for target engagement and selectivity for MINA53 over KDM4-6. The MINA53 inhibitors show antiproliferative activity with solid cancer lines and sensitize cancer cells to conventional chemotherapy, suggesting that further work investigating their potential in combination therapies is warranted.

Glycan Epitopes and Potential Glycoside Antagonists of DC-SIGN Involved in COVID-19: In Silico Study

Glycosylation is an important post-translational modification that affects a wide variety of physiological functions. DC-SIGN (Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin) is a protein expressed in antigen-presenting cells that recognizes a variety of glycan epitopes. Until now, the binding of DC-SIGN to SARS-CoV-2 Spike glycoprotein has been reported in various articles and is regarded to be a factor in systemic infection and cytokine storm. The mechanism of DC-SIGN recognition offers an alternative method for discovering new medication for COVID-19 treatment. Here, we discovered three potential pockets that hold different glycan epitopes by performing molecular dynamics simulations of previously reported oligosaccharides. The "EPN" motif, "NDD" motif, and Glu354 form the most critical pocket, which is known as the Core site. We proposed that the type of glycan epitopes, rather than the precise amino acid sequence, determines the recognition. Furthermore, we deduced that oligosaccharides could occupy an additional site, which adds to their higher affinity than monosaccharides. Based on our findings and previously described glycoforms on the SARS-CoV-2 Spike, we predicted the potential glycan epitopes for DC-SIGN. It suggested that glycan epitopes could be recognized at multiple sites, not just Asn234, Asn149 and Asn343. Subsequently, we found that Saikosaponin A and Liquiritin, two plant glycosides, were promising DC-SIGN antagonists in silico.

gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS

Molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area is one of the most popular methods to estimate binding free energies. This method has been proven to balance accuracy and computational efficiency, especially when dealing with large systems. As a result of its popularity, several programs have been developed for performing MM/PB(GB)SA calculations within the GROMACS community. These programs, however, present several limitations. Here we present gmx_MMPBSA, a new tool to perform end-state free energy calculations from GROMACS molecular dynamics trajectories. gmx_MMPBSA provides the user with several options, including binding free energy calculations with different solvation models (PB, GB, or 3D-RISM), stability calculations, computational alanine scanning, entropy corrections, and binding free energy decomposition. Noteworthy, several promising methodologies to calculate relative binding free energies such as alanine scanning with variable dielectric constant and interaction entropy have also been implemented in gmx_MMPBSA. Two additional tools-gmx_MMPBSA_test and gmx_MMPBSA_ana-have been integrated within gmx_MMPBSA to improve its usability. Multiple illustrating examples can be accessed through gmx_MMPBSA_test, while gmx_MMPBSA_ana provides fast, easy, and efficient access to different graphics plotted from gmx_MMPBSA output files. The latest version (v1.4.3, 26/05/2021) is available free of charge (documentation, test files, and tutorials included) at https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA.

Application of Molecular Dynamics Simulations in the Analysis of Cyclodextrin Complexes

Cyclodextrins (CDs) are highly respected for their ability to form inclusion complexes via host-guest noncovalent interactions and, thus, ensofance other molecular properties. Various molecular modeling methods have found their applications in the analysis of those complexes. However, as showed in this review, molecular dynamics (MD) simulations could provide the information unobtainable by any other means. It is therefore not surprising that published works on MD simulations used in this field have rapidly increased since the early 2010s. This review provides an overview of the successful applications of MD simulations in the studies on CD complexes. Information that is crucial for MD simulations, such as application of force fields, the length of the simulation, or solvent treatment method, are thoroughly discussed. Therefore, this work can serve as a guide to properly set up such calculations and analyze their results.

Rational design of selective HDAC2 inhibitors for liver cancer treatment: computational insights into the selectivity mechanism through molecular dynamics simulations and QM/MM calculations

The rational design of selective histone deacetylase 2 (HDAC2) inhibitors is beneficial for the therapeutic treatment of liver cancer, though HDAC2 is highly homologous to HDAC8, which may lead to undesired side effects due to the pan-inhibition towards HDAC2 and HDAC8. To clarify the structural basis of selective inhibition towards HDAC2 over HDAC8, we utilized multiple in silico strategies, including sequence alignment, structural comparison, molecular docking, molecular dynamics simulations, free energy calculations, alanine scanning mutagenesis, pharmacophore modeling, protein contacts atlas analysis and QM/MM calculations to study the binding patterns of HDAC2/8 selective inhibitors. Through the whole process described above, it is found that although HDAC2 has conserved GLY154 and PHE210 that also exist within HDAC8, namely GLY151 and PHE208, the two isoforms exhibit diverse binding modes towards their inhibitors. Typically, HDAC2 inhibitors interact with the Zn²⁺ ions through the core chelate group, while HDAC8 inhibitors adopt a bent conformation within the HDAC8 pocket that inclines to be in contact with the Zn²⁺ ions through the terminal hydroxamic acid group. In summary, our data comprehensively elucidate the selectivity mechanism towards HDAC2 over HDAC8, which would guide the rational design of selective HDAC2 inhibitors for liver cancer treatment.

Tissue-Specific Regulation of HNK-1 Biosynthesis by Bisecting GlcNAc

Human natural killer-1 (HNK-1) is a sulfated glyco-epitope regulating cell adhesion and synaptic functions. HNK-1 and its non-sulfated forms, which are specifically expressed in the brain and the kidney, respectively, are distinctly biosynthesized by two homologous glycosyltransferases: GlcAT-P in the brain and GlcAT-S in the kidney. However, it is largely unclear how the activity of these isozymes is regulated in vivo. We recently found that bisecting GlcNAc, a branching sugar in N-glycan, suppresses both GlcAT-P activity and HNK-1 expression in the brain. Here, we observed that the expression of non-sulfated HNK-1 in the kidney is unexpectedly unaltered in mutant mice lacking bisecting GlcNAc. This suggests that the biosynthesis of HNK-1 in the brain and the kidney are differentially regulated by bisecting GlcNAc. Mechanistically, in vitro activity assays demonstrated that bisecting GlcNAc inhibits the activity of GlcAT-P but not that of GlcAT-S. Furthermore, molecular dynamics simulation showed that GlcAT-P binds poorly to bisected N-glycan substrates, whereas GlcAT-S binds similarly to bisected and non-bisected N-glycans. These findings revealed the difference of the highly homologous isozymes for HNK-1 synthesis, highlighting the novel mechanism of the tissue-specific regulation of HNK-1 synthesis by bisecting GlcNAc.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

The milk-derived lactoferrin inhibits V-ATPase activity by targeting its V1 domain

Lactoferrin (Lf), a bioactive milk protein, exhibits strong anticancer and antifungal activities. The search for Lf targets and mechanisms of action is of utmost importance to enhance its effective applications. A common feature among Lf-treated cancer and fungal cells is the inhibition of a proton pump called V-ATPase. Lf-driven V-ATPase inhibition leads to cytosolic acidification, ultimately causing cell death of cancer and fungal cells. Given that a detailed elucidation of how Lf and V-ATPase interact is still missing, herein we aimed to fill this gap by employing a five-stage computational approach. Molecular dynamics simulations of both proteins were performed to obtain a robust sampling of their conformational landscape, followed by clustering, which allowed retrieving representative structures, to then perform protein-protein docking. Subsequently, molecular dynamics simulations of the docked complexes and free binding energy calculations were carried out to evaluate the dynamic binding process and build a final ranking based on the binding affinities. Detailed atomist analysis of the top ranked complexes clearly indicates that Lf binds to the V₁ cytosolic domain of V-ATPase. Particularly, our data suggest that Lf binds to the interfaces between A/B subunits, where the ATP hydrolysis occurs, thus inhibiting this process. The free energy decomposition analysis further identified key binding residues that will certainly aid in the rational design of follow-up experimental studies, hence bridging computational and experimental biochemistry.

Optimized Halogen Atomic Radii for PBSA Calculations Using Off-Center Point Charges

In force-field methods, the usage of off-center point charges, also called extra points (EPs), is a common strategy to tackle the anisotropy of the electrostatic potential of covalently bonded halogens (X), thus allowing the description of halogen bonds (XBs) at the molecular mechanics/molecular dynamics (MM/MD) level. Diverse EP implementations exist in the literature differing on the charge sets and/or the X-EP distances. Poisson-Boltzmann and surface area (PBSA) calculations can be used to obtain solvation free energies (ΔG_solv) of small molecules, often to compute binding free energies (ΔG_bind) at the MM-PBSA level. This method depends, among other parameters, on the empirical assignment of atomic radii (PB radii). Given the multiplicity of off-center point-charge models and the lack of specific PB radii for halogens compatible with such implementations, in this work, we assessed the performance of PBSA calculations for the estimation of ΔG_solv values in water (ΔG_hyd), also conducting an optimization of the halogen PB radii (Cl, Br, and I) for each EP model. We not only expand the usage of EP models in the scope of the general AMBER force field (GAFF) but also provide the first optimized halogen PB radii in the context of the CHARMM general force field (CGenFF), thus contributing to improving the description of halogenated compounds in PBSA calculations.

Insights Into Mutations Induced Conformational Changes and Rearrangement of Fe2+ Ion in pncA Gene of Mycobacterium tuberculosis to Decipher the Mechanism of Resistance to Pyrazinamide

Pyrazinamide (PZA) is the first-line drug commonly used in treating Mycobacterium tuberculosis (Mtb) infections and reduces treatment time by 33%. This prodrug is activated and converted to an active form, Pyrazinoic acid (POA), by Pyrazinamidase (PZase) enzyme. Mtb resistance to PZA is the outcome of mutations frequently reported in pncA, rpsA, and panD genes. Among the mentioned genes, pncA mutations contribute to 72-99% of the total resistance to PZA. Thus, considering the vital importance of this gene in PZA resistance, its frequent mutations (D49N, Y64S, W68G, and F94A) were investigated through in-depth computational techniques to put conclusions that might be useful for new scaffolds design or structure optimization to improve the efficacy of the available drugs. Mutants and wild type PZase were used in extensive and long-run molecular dynamics simulations in triplicate to disclose the resistance mechanism induced by the above-mentioned point mutations. Our analysis suggests that these mutations alter the internal dynamics of PZase and hinder the correct orientation of PZA to the enzyme. Consequently, the PZA has a low binding energy score with the mutants compared with the wild type PZase. These mutations were also reported to affect the binding of Fe2+ ion and its coordinated residues. Conformational dynamics also revealed that β-strand two is flipped, which is significant in Fe2+ binding. MM-GBSA analysis confirmed that these mutations significantly decreased the binding of PZA. In conclusion, these mutations cause conformation alterations and deformities that lead to PZA resistance.

Thermochemical and Quantum Descriptor Calculations for Gaining Insight into Ricin Toxin A (RTA) Inhibitors

In this work, we performed a study to assess the interactions between the ricin toxin A (RTA) subunit of ricin and some of its inhibitors using modern semiempirical quantum chemistry and ONIOM quantum mechanics/molecular mechanics (QM/MM) methods. Two approaches were followed (calculation of binding enthalpies, ΔH _bind, and reactivity quantum chemical descriptors) and compared with the respective half-maximal inhibitory concentration (IC₅₀) experimental data, to gain insight into RTA inhibitors and verify which quantum chemical method would better describe RTA-ligand interactions. The geometries for all RTA-ligand complexes were obtained after running classical molecular dynamics simulations in aqueous media. We found that single-point energy calculations of ΔH _bind with the PM6-DH+, PM6-D3H4, and PM7 semiempirical methods and ONIOM QM/MM presented a good correlation with the IC₅₀ data. We also observed, however, that the correlation decreased significantly when we calculated ΔH _bind after full-atom geometry optimization with all semiempirical methods. Based on the results from reactivity descriptors calculations for the cases studied, we noted that both types of interactions, molecular overlap and electrostatic interactions, play significant roles in the overall affinity of these ligands for the RTA binding pocket.

Exploring of paritaprevir and glecaprevir resistance due to A156T mutation of HCV NS3/4A protease: molecular dynamics simulation study

Hepatitis C virus (HCV) NS3/4A serine protease is a promising drug target for the discovery of anti-HCV drugs. However, its amino acid mutations, particularly A156T, commonly lead to rapid emergence of drug resistance. Paritaprevir and glecaprevir, the newly FDA-approved HCV drugs, exhibit distinct resistance profiles against the A156T mutation of HCV NS3/4A serine protease. To illustrate their different molecular resistance mechanisms, molecular dynamics simulations and binding free energy calculations were carried out on the two compounds complexed with both wild-type (WT) and A156T variants of HCV NS3/4A protease. QM/MM-GBSA-based binding free energy calculations revealed that the binding affinities of paritaprevir and glecaprevir towards A156T NS3/4A were significantly reduced by ∼4 kcal/mol with respect to their WT complexes, which were in line with the experimental resistance folds. Moreover, the relatively weak intermolecular interactions with amino acids such as H57, R155, and T156 of NS3 protein, the steric effect and the destabilized protein binding surface, which is caused by the loss of salt bridge between R123 and D168, are the main contributions for the higher fold-loss in potency of glecaprevir due to A156T mutation. An insight into the difference of molecular mechanism of drug resistance against the A156T substitution among the two classes of serine protease inhibitors could be useful for further optimization of new generation HCV NS3/4A inhibitors with enhanced inhibitory potency.Communicated by Ramaswamy H. Sarma.

Carbohydrate-Protein Interactions: Advances and Challenges

A carbohydrate, also called saccharide in biochemistry, is a biomolecule consisting of carbon (C), hydrogen (H) and oxygen (O) atoms. For example, sugars are low molecular-weight carbohydrates, and starches are high molecular-weight carbohydrates. Carbohydrates are the most abundant organic substances in nature and essential constituents of all living things. Protein-carbohydrate interactions play important roles in many biological processes, such as cell growth, differentiation, and aggregation. They also have broad applications in pharmaceutical drug design. In this review, we will summarize the characteristic features of protein-carbohydrate interactions and review the computational methods for structure prediction, energy calculations, and kinetic studies of protein-carbohydrate complexes. Finally, we will discuss the challenges in this field.

Pick's Tau Fibril Shows Multiple Distinct PET Probe Binding Sites: Insights from Computational Modelling

In recent years, it has been realized that the tau protein is a key player in multiple neurodegenerative diseases. Positron emission tomography (PET) radiotracers that bind to tau filaments in Alzheimer's disease (AD) are in common use, but PET tracers binding to tau filaments of rarer, age-related dementias, such as Pick's disease, have not been widely explored. To design disease-specific and tau-selective PET tracers, it is important to determine where and how PET tracers bind to tau filaments. In this paper, we present the first molecular modelling study on PET probe binding to the structured core of tau filaments from a patient with Pick's disease (TauPiD). We have used docking, molecular dynamics simulations, binding-affinity and tunnel calculations to explore TauPiD binding sites, binding modes, and binding energies of PET probes (AV-1451, MK-6240, PBB3, PM-PBB3, THK-5351 and PiB) with TauPiD. The probes bind to TauPiD at multiple surface binding sites as well as in a cavity binding site. The probes show unique surface binding patterns, and, out of them all, PM-PBB3 proves to bind the strongest. The findings suggest that our computational workflow of structural and dynamic details of the tau filaments has potential for the rational design of TauPiD specific PET tracers.

High-Contrast In Vivo Imaging of Tau Pathologies in Alzheimer's and Non-Alzheimer's Disease Tauopathies

A panel of radiochemicals has enabled in vivo positron emission tomography (PET) of tau pathologies in Alzheimer's disease (AD), although sensitive detection of frontotemporal lobar degeneration (FTLD) tau inclusions has been unsuccessful. Here, we generated an imaging probe, PM-PBB3, for capturing diverse tau deposits. In vitro assays demonstrated the reactivity of this compound with tau pathologies in AD and FTLD. We could also utilize PM-PBB3 for optical/PET imaging of a living murine tauopathy model. A subsequent clinical PET study revealed increased binding of ¹⁸F-PM-PBB3 in diseased patients, reflecting cortical-dominant AD and subcortical-dominant progressive supranuclear palsy (PSP) tau topologies. Notably, the in vivo reactivity of ¹⁸F-PM-PBB3 with FTLD tau inclusion was strongly supported by neuropathological examinations of brains derived from Pick's disease, PSP, and corticobasal degeneration patients who underwent PET scans. Finally, visual inspection of ¹⁸F-PM-PBB3-PET images was indicated to facilitate individually based identification of diverse clinical phenotypes of FTLD on a neuropathological basis.

Structural insights into the mechanism of RNA recognition by the N-terminal RNA-binding domain of the SARS-CoV-2 nucleocapsid phosphoprotein

The emergence of recent SARS-CoV-2 has become a global health issue. This single-stranded positive-sense RNA virus is continuously spreading with increasing morbidities and mortalities. The proteome of this virus contains four structural and sixteen nonstructural proteins that ensure the replication of the virus in the host cell. However, the role of phosphoprotein (N) in RNA recognition, replicating, transcribing the viral genome, and modulating the host immune response is indispensable. Recently, the NMR structure of the N-terminal domain of the Nucleocapsid Phosphoprotein has been reported, but its precise structural mechanism of how the ssRNA interacts with it is not reported yet. Therefore, here, we have used an integrated computational pipeline to identify the key residues, which play an essential role in RNA recognition. We generated multiple variants by using an alanine scanning strategy and performed an extensive simulation for each system to signify the role of each interfacial residue. Our analyses suggest that residues T57A, H59A, S105A, R107A, F171A, and Y172A significantly affected the dynamics and binding of RNA. Furthermore, per-residue energy decomposition analysis suggests that residues T57, H59, S105 and R107 are the key hotspots for drug discovery. Thus, these residues may be useful as potential pharmacophores in drug designing.

Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study

The recent outbreak of novel coronavirus disease-19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D structures of key viral proteins are resolved. The virus causing COVID-19 is SARS-CoV-2. Taking advantage of a recently released crystal structure of SARS-CoV-2 main protease in complex with a covalently bonded inhibitor, N3 (Liu et al., 10.2210/pdb6LU7/pdb), I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an end point method called MM-PBSA-WSAS (molecular mechanics/Poisson-Boltzmann surface area/weighted solvent-accessible surface area; Wang, Chem. Rev. 2019, 119, 9478; Wang, Curr. Comput.-Aided Drug Des. 2006, 2, 287; Wang; ; Hou J. Chem. Inf. Model., 2012, 52, 1199). Several promising known drugs stand out as potential inhibitors of SARS-CoV-2 main protease, including carfilzomib, eravacycline, valrubicin, lopinavir, and elbasvir. Carfilzomib, an approved anticancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.8 kcal/mol) is not nearly as low as that of the neutral form (-7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.9 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hot spot residue, His41, is a conserved residue across many viruses including SARS-CoV, SARS-CoV-2, MERS-CoV, and hepatitis C virus (HCV). The findings of this study can facilitate rational drug design targeting the SARS-CoV-2 main protease.

Investigating Conformational Dynamics of Lewis Y Oligosaccharides and Elucidating Blood Group Dependency of Cholera Using Molecular Dynamics

Cholera is caused by Vibrio cholerae and is an example of a blood-group-dependent disease. Recent studies suggest that the receptor-binding B subunit of the cholera toxin (CT) binds histo-blood group antigens at a secondary binding site. Herein, we studied the conformational dynamics of Lewis Y (Le^Y) oligosaccharides, H-tetrasaccharides and A-pentasaccharides, in aqueous solution by conducting accelerated molecular dynamics (aMD) simulations. The flexible nature of both oligosaccharides was displayed in aMD simulations. Furthermore, aMD simulations revealed that for both oligosaccharides in the free form, ⁴C₁ and ¹C₄ puckers were sampled for all but GalNAc monosaccharides, while either the ⁴C₁ (GlcNAc, Gal, GalNAc) or ¹C₄ (Fuc2, Fuc3) pucker was sampled in the CT-bound forms. In aMD, the complete transition from the ⁴C₁ to ¹C₄ pucker was sampled for GlcNAc and Gal in both oligosaccharides. Further, we have observed a transition from the open to closed conformer in the case of A-pentasaccharide, while H-tetrasaccharide remains in the open conformation throughout the simulation. Both oligosaccharides adopted an open conformation in the CT binding site. Moreover, we have investigated the molecular basis of recognition of Le^Y oligosaccharides by the B subunit of the cholera toxin of classical and El Tor biotypes using the molecular mechanics generalized Born surface area (MM/GBSA) scheme. The O blood group determinant, H-tetrasaccharide, exhibits a stronger affinity to both biotypes compared to the A blood group determinant, A-pentasaccharide, which agrees with the experimental data. The difference in binding free energy between O and A blood group determinants mainly arises due to the increased entropic cost and desolvation energy in the case of A-pentasaccharide compared to that of H-tetrasaccharide. Our study also reveals that the terminal Fuc3 contributes most to the binding free energy compared to other carbohydrate residues as it forms multiple hydrogen bonds with CT. Overall, our study might help in designing glycomimetic drugs targeting the cholera toxin.

Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study

The recent outbreak of novel coronavirus disease -19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D-structures of key virous proteins are resolved. Taking the advantage of a recently released crystal structure of COVID-19 protease in complex with a covalently-bonded inhibitor, N3,1 I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an endpoint method called MM-PBSA-WSAS.2-4 Several promising known drugs stand out as potential inhibitors of COVID-19 protease, including Carfilzomib, Eravacycline, Valrubicin, Lopinavir and Elbasvir. Carfilzomib, an approved anti-cancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.82 kcal/mol. Streptomycin, an antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.82 kcal/mol) is not nearly as low as that of the neutral form (-7.92 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.86 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hotspot residue HIS41, is a conserved residue across many viruses including COVID-19, SARS, MERS, and HCV. The findings of this study can facilitate rational drug design targeting the COVID-19 protease.

Molecular basis of SMAC-XIAP binding and the effect of electrostatic polarization

X-chromosome-linked inhibitor of apoptosis (XIAP) inhibits cell apoptosis. Overexpression of XIAP is widely found in human cancers. Second mitochondria-derived activator of caspase (SMAC) protein inhibits XIAP through binding with Baculovirus Inhibitor of apoptosis protein Repeat (BIR) 3 or BIR2 domain of XIAP. In this study, molecular dynamics (MD) simulations and the alanine scanning calculations by MM-GBSA_IE method were used to investigate the protein-peptide interaction between BIR3 and BIR2 domains of XIAP and SMAC peptide. Energetic contribution of each binding residue is calculated and hotspots on both XIAP and SMAC were identified using computational alanine scanning with interaction entropy method. We found that electrostatic polarization is important in stabilizing the protein-protein complex structure in MD simulation. By using polarized protein-specific charges, much better agreement with experimental result is obtained for calculated binding free energies compared to those using standard (nonpolarizable) AMBER force field. In particular, excellent correlation between calculated binding free energies in alanine scanning with mutational experimental data was obtained for BIR3/SMAC binding.Communicated by Ramaswamy H. Sarma.