Binding Free Energy Calculations Research Articles

New Indole-based Phenylthiazolyl-2,4-dihydropyrazolones as Tubulin polymerization inhibitors: Multicomponent synthesis, cytotoxicity evaluation, and in silico studies.

A facile multicomponent synthesis of new indole-based phenylthiazolyl-dihydropyrazolone hybrids, their structural characterization, biological evaluation, and in silico investigations as anticancer agents are reported. Lead molecule 5i of the series showed potent activity against MCF-7 breast cancer cells with an IC50 of 3.92± 0.01µM while showing minimal toxicity to normal human lung cells (IC50 = 69.85 ± 3.95 µM). Further studies show that the compound exhibits antiproliferative activity by inducing apoptosis in MCF-7 cancer cells. The wound healing assay indicated impaired cell migration under the concentration-dependent dosage. The lead molecule 5i also successfully inhibited the tubulin polymerase enzyme with an IC50 of 4.16 ± 0.18 µM. A flow cytometric assay indicated compound 5i induced apoptosis through G0 phase cell cycle arrest. The binding mode and interactions of the compound with the tubulin were predicted by molecular modelling and calculating binding free energies. These findings explain the current series as a new class of microtubule polymerization inhibitors with anticancer activity suitable for developing anticancer agents targeting tubulin.

Discovering Lassa virus nucleoprotein inhibitors via in silico drug repositioning approach

Lassa fever, caused by the zoonotic Lassa virus (LASV), poses a significant health threat in Africa, leading to thousands of infections and deaths annually and has the potential to spread to other parts of the world. Despite the urgency for effective treatments, there are currently no approved drugs or vaccines for Lassa fever. LASV possesses a unique negative-sense RNA genome, and NP plays a crucial role in viral assembly and infection. Crystallographic analysis reveals distinct domains in NP, with the N-terminal domain involved in RNA binding and the C-terminal domain exhibiting exoribonuclease activity, suppressing type I interferon-mediated immune responses. This study explores the potential of repurposing existing FDA-approved drugs by targeting the N-terminal domain of LASV’s nucleoprotein (NP). Docking simulations and molecular dynamics experiments were conducted, revealing promising interactions between NP and widely used and well tolerated drugs such as metacycline, eltrombopag, glimepiride, lurasidone, paliperidone, prednisone, doxazosin, flavin mononucleotide, and pimozide. These drugs exhibited stable binding throughout 100 ns simulations, with interactions resembling those observed with the natural ligand, dTTP. Binding free energy calculations identified key amino acids, particularly Phe176 and Arg300, as crucial for drug-NP interactions. Notably, drugs like FMN, prednisone, metacycline, pimozide, and glimepiride displayed binding affinities comparable to dTTP, suggesting their potential as LASV inhibitors. The study underscores the importance of further experimental and clinical validation of these in silico findings. The identified drugs present promising candidates for potential treatments for Lassa fever, addressing the current gap in approved therapeutics for this life-threatening infectious disease.

Molecular insights from structural dynamics of HER2-inhibitor complexes pave the way for new breast cancer drugs

The human epidermal growth factor receptor 2 (HER2) is closely associated with the development and progression of breast cancer, making it a critical target for therapeutic interventions. In this study, we employed a comprehensive computational drug discovery strategy to identify potential inhibitors of HER2. Our approach combined virtual screening, re-docking procedures, molecular dynamics (MD) simulations, and free energy landscape analysis using principal component analysis (PCA). From the extensive PubChem library, we initially screened 733 compounds for their binding potential to HER2, using docking scores as a primary filter. These scores ranged notably from −11.172 to −7.028 kcal/mol, indicating substantial binding capacities. Following this screening, we selected four promising compounds (PubChem CID 166029206, 166544027, 21031510, and 11712721) along with a control compound (70I) for in-depth analysis. Utilizing the Amber software suite for MD simulations, we conducted 200-nanosecond simulations to assess the interactions and binding efficiencies of these selected compounds with HER2. We analysed the molecular interactions through various parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond formation patterns, free binding energy calculations. The PCA-based free energy landscape analysis revealed that these compounds consistently occupied a distinct low-energy basin, indicating their high stability and strong binding affinity for the HER2. This detailed analysis provided insights into the stability and conformational dynamics of these potential inhibitors when bound to the HER2. Our findings pave the way for further experimental validation and development of these compounds as therapeutic agents in breast cancer treatment.

Identification of Malaria-Selective Proteasome β5 Inhibitors Through Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation

Malaria remains a global health challenge, with increasing resistance to frontline antimalarial treatments such as artemisinin (ART) threatening the efficacy of current therapies. In this study, we investigated the potential of FDA-approved drugs to selectively inhibit the malarial proteasome, a novel target for antimalarial drug development. By leveraging pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations, and binding free-energy calculations, we screened a library of compounds to identify inhibitors selective for the Plasmodium proteasome over the human proteasome. Our results highlighted Argatroban, LM-3632, Atazanavir Sulfate, and Pemetrexed Hydrate as promising candidates, with Argatroban and Pemetrexed Hydrate showing the highest binding affinity and selectivity toward the malarial proteasome. MD simulation and gmx_MMPBSA analysis confirmed the compounds’ ability to remain within the active site of the malarial proteasome, while some exited or exhibited reduced stability within the human proteasome. This study underscores the potential of proteasome-targeting drugs for overcoming malarial drug resistance and paves the way for the further optimization of these compounds.

Computational investigations of flavonoids as ALDH isoform inhibitors for treatment of cancer

ABSTRACT Human aldehyde dehydrogenases (ALDHs) are a group of 19 isoforms often overexpressed in cancer stem cells (CSCs). These enzymes play critical roles in CSC protection, maintenance, cancer progression, therapeutic resistance, and poor prognosis. Thus, targeting ALDH isoforms offers potential for innovative cancer treatments. Flavonoids, known for their ability to affect multiple cancer-related pathways, have shown anticancer activity by downregulating specific ALDH isoforms. This study aimed to evaluate 830 flavonoids from the PubChem database against five ALDH isoforms (ALDH1A1, ALDH1A2, ALDH1A3, ALDH2, ALDH3A1) using computational methods to identify potent inhibitors. Extra precision (XP) Glide docking and MM-GBSA free binding energy calculations identified several flavonoids with high binding affinities. MD simulation highlighted flavonoids 1, 2, 18, 27, and 42 as potential specific inhibitors for each isoform, respectively. Flavonoid 10 showed high binding affinities for ALDH1A2, ALDH1A3, and ALDH3A1, emerging as a potential multi-ALDH inhibitor. ADMET property evaluation indicated that the promising hits have acceptable drug-like profiles, but further optimization is needed to enhance their therapeutic efficacy and reduce toxicity, making them more effective ALDH inhibitors for future cancer treatment.

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB1 cannabinoid ligands.

The substantial increase in the number of active and inactive-state CB1 receptor experimental structures has provided opportunities for CB1 drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB1 receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (rMM/GBSA = 0.433 - 0.652 vs. rMM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energyminimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB1 receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors. The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30ns production simulations using GROMACS 2018 at 300K and 1atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.

Comparison of Methodologies for Absolute Binding Free Energy Calculations of Ligands to Intrinsically Disordered Proteins.

Modulating the function of Intrinsically Disordered Proteins (IDPs) with small molecules is of considerable importance given the crucial roles of IDPs in the pathophysiology of numerous diseases. Reported binding affinities for ligands to diverse IDPs vary broadly, and little is known about the detailed molecular mechanisms that underpin ligand efficacy. Molecular simulations of IDP ligand binding mechanisms can help us understand the mode of action of small molecule inhibitors of IDP function, but it is still unclear how binding energies can be modeled rigorously for such a flexible class of proteins. Here, we compare alchemical absolute binding free energy calculations (ABFE) and Markov-State Modeling (MSM) protocols to model the binding of the small molecule 10058-F4 to a disordered peptide extracted from a segment of the oncoprotein c-Myc. The ABFE results produce binding energy estimates that are sensitive to the choice of reference structure. In contrast, the MSM results produce more reproducible binding energy estimates consistent with weak mM binding affinities and transient intermolecular contacts reported in the literature.

Scaffold Hopping Method for Design and Development of Potential Allosteric AKT Inhibitors.

Targeting AKT is a practical strategy for cancer therapy in many cancer types. Targeted inhibitors of AKT are attractive solutions for inhibiting the interconnected signaling pathways, like PI3K/Akt/mTOR. Allosteric inhibitors are more desirable among different classes of AKT inhibitors as they could be more specific with fewer off-target proteins. In this study, a ligand/structure-based pipeline was developed to design new allosteric AKT inhibitors by employing the core hopping method. Triciribine, a traditional allosteric AKT inhibitor was used as the template, and the FDA-approved kinase inhibitors for cancer treatment were considered as the cores. The allosteric site in the crystal structure of AKT1 was used to screen the designed compounds. The results were further evaluated using molecular docking, ADME/T analysis, molecular dynamics (MD) simulation, and binding free energy calculations. The outcomes introduced 24 newly designed inhibitors, amongst which three compounds C6, C20, and C16 showed remarkable binding affinity to AKT1. While the docking scores for triciribine was around -8.6 kcal/mol, the docking scores of these compounds were about -11 to -13 kcal/mol. The MD results indicated that designed compounds target the essential residues of the PH domain and kinase domain of AKT, such as Trp80, Thr211, Tyr272, Asp274, and Asp292. Scaffold hopping is a tremendous tool for designing novel anti-cancer agents by improving already known and potential drug compounds. The designed compounds are worth to be examined by experimental investigation in vitro and in vivo.

Structural insights into the toxicity of type II ribosome inactivating proteins (RIPs): a molecular dynamics study

Ribosome Inactivating Proteins (RIPs) act by irreversibly depurinating the 28S rRNA ricin-sarcin loop (SRL) of the eukaryotic ribosome resulting in protein synthesis inhibition. In general, they consist of two variants: Type I which is single chained (∼30 kDa), and Type II, a more toxic variant which is a Type I N-glycosidase chain covalently linked to a lectin chain. These proteins are believed to play a pivotal role in defence mechanisms. Intriguingly, non-toxic variants of such toxic proteins do exist in nature. To explore their mode of action, in the present study we have selected three toxic (Ricin, Ebulin and HmRIP) as well as two non-toxic (BGSL and SGSL) RIPs and performed molecular docking and molecular dynamic simulations with the SRL loop. This study throws light on the structural stability and plasticity of the toxic and non-toxic RIP complexes. Furthermore, analysis of the active site cavity volume and binding free energy calculations reveal that the SRL, particularly the specific adenine (A4605), is relatively unstable in the case of non-toxic RIPs which is also supported by the free binding energy calculations, and the pocket size analysis indicates the abnormal increase in active site cavity volume of non-toxic RIPs with time. This first-of-its-kind comprehensive study of toxic and non-toxic RIPs gives insights about the mode of action and the dynamic nature of their interaction with the SRL loop. These observations will be helpful in the development of toxoids against RIPs and also in designing novel therapeutic approaches against human diseases.

Analysis of Glycan Recognition by Concanavalin A Using Absolute Binding Free Energy Calculations.

Carbohydrates are key biological mediators of molecular recognition and signaling processes. In this case study, we explore the ability of absolute binding free energy (ABFE) calculations to predict the affinities of a set of five related carbohydrate ligands for the lectin protein, concanavalin A, ranging from 27-atom monosaccharides to a 120-atom complex-type N-linked glycan core pentasaccharide. ABFE calculations quantitatively rank and estimate the affinity of the ligands in relation to microcalorimetry, with a mean signed error in the binding free energy of -0.63 ± 0.04 kcal/mol. Consequently, the diminished binding efficiencies of the larger carbohydrate ligands are closely reproduced: the ligand efficiency values from isothermal titration calorimetry for the glycan core pentasaccharide and its constituent trisaccharide and monosaccharide compounds are respectively -0.14, -0.22, and -0.41 kcal/mol per heavy atom. ABFE calculations predict these ligand efficiencies to be -0.14 ± 0.02, -0.24 ± 0.03, and -0.46 ± 0.06 kcal/mol per heavy atom, respectively. Consequently, the ABFE method correctly identifies the high affinity of the key anchoring mannose residue and the negligible contribution to binding of both β-GlcNAc arms of the pentasaccharide. While challenges remain in sampling the conformation and interactions of these polar, flexible, and weakly bound ligands, we nevertheless find that the ABFE method performs well for this lectin system. The approach shows promise as a quantitative tool for predicting and deconvoluting carbohydrate-protein interactions, with potential application to design of therapeutics, vaccines, and diagnostics.

Computational modeling to understand the interaction of TMPyP4 with a G-quadruplex

The potential of small molecules to bind to G-quadruplex-forming sequences in oncogene promoter regions, thereby regulating their structural equilibrium, has been explored as a promising strategy for cancer chemotherapy. The model drug 5,10,15,20-tetrakis-(N-methyl-4-pyridyl)porphine (TMPyP4) has been shown to have an affinity toward G-quadruplex DNA. However, the precise sites and modes of TMPyP4 binding to G-quadruplex DNA remain a subject of debate. In this study, we focus on identifying potential binding sites on a mutant c-MYC sequence known to fold into a single 1:2:1 loop isomer quadruplex. Our findings provide insights into the 4:1 stoichiometry reported for TMPyP4 binding to this G-quadruplex. Binding enthalpy and free energy calculations show that intercalation of a TMPyP4 molecule between the quadruplexes is thermodynamically favorable. Our calculations suggest that two of the binding sites are located at the top and bottom of the quadruplex, respectively, while the remaining two are likely intercalations.

Computational exploration of novel ketoprofen derivatives: Molecular dynamics simulations and MM-PBSA calculations for COX-2 inhibition as promising anti-inflammatory drugs

Computer-aided drug design is widely employed to identify novel compounds for therapeutic applications. Ketoprofen (KTP), a commonly used and marketed nonsteroidal anti-inflammatory drug (NSAID), is effective in treating pain, fever, inflammation, and some cancers. In this research, we explored the behavior of six analogues designed by structurally modifying the KTP molecule. Specifically, KTP-A and KTP-B contain a –CN group at the ortho position, KTP-C and KTP-D have a –CN group at the meta position, and KTP-E and KTP-F feature a –CF3 group at the meta position. To assess these analogues, we conducted molecular dynamics simulations (MD) to study their inhibitory effects on human cyclooxygenase 2 (COX-2), providing detailed insights into the structure and dynamics of the protein both with and without ligands. MD simulation, enhanced by technological advances, has proven to be a powerful tool for new drug discovery. We further quantified the binding affinity of these drug molecules toward COX-2 using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The dynamic properties were evaluated through analyses of root mean square deviations (RMSD), root mean square fluctuations (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), covariance matrix, principal component analysis (PCA), and Gibbs free energy landscapes (FEL). Ultimately, this study confirms that the six KTP derivatives are promising candidates for the treatment of inflammation, with KTP-B standing out as particularly effective.

Proanthocyanidin Regulates NETosis and Inhibits the Growth and Proliferation of Liver Cancer Cells - In Vivo, In Vitro and In Silico Investigation.

Liver cancer ranks third in global cancer-related mortality, with about 700,000 deaths recorded yearly, making it one of the most common cancers worldwide. Even though prognoses differ according to the severity of the diseases, many patients now exhibit an increased life cycle since the implementation of chemotherapy. In the current study, we investigated the effect of proanthocyanidin ‒a polyphenol molecule found in many plants‒ on the proliferation and invasion of liver cancer cells. In particular, we determined the effect of proanthocyanidin on the serum levels of four strategic liver cancer target, TNFα, IL-6, cfDNA, and IL-1β. Further molecular insight on the inhibitory mechanism of proanthocyanidin against TNFα, IL-6, and IL-1β was obtained via molecular docking, molecular dynamics simulations and binding free energy calculations. Results showed that proanthocyanidin inhibited the growth of HepG2 and HEP3B cells, and effectively reduced clonogenic survival and invasion potential when compared to control cells. Proanthocyanidin was also found to suppress the expression of Bcl-2 (26 kDa) protein in HepG2 cells, while increasing the expression of Bax (21 kDa). Molecular dynamics (MD) and thermodynamic binding free energy calculations showed that proanthocyanidin maintained stable binding within the active site of target proteins across the entire 100 ns MD simulation period, and its binding affinity outscored respective control molecules.In conclusion, the multifaceted analysis showcased in this study demonstrated promising anti-cancer effect of proanthocyanidin on HepG2 and HEP3B cancer cells, highlighting its potential as a viable liver cancer therapeutic alternative.

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.

Structure-Based Discovery of Phytocompounds from Azadirachta indica as Potential Inhibitors of Thioredoxin Glutathione Reductase in Schistosoma mansoni.

Schistosomiasis, a parasitic disease caused by Schistosoma species such as S. haematobium, S. mansoni, and S. japonicum, poses a significant global health burden. The thioredoxin glutathione reductase (TGR) enzyme, crucial for maintaining the parasite's redox balance and preventing oxidative stress, has been identified as a promising target for anti-schistosomal drug development. This study aims to identify potential TGR inhibitors from Azadirachta indica phytochemicals using molecular modeling approaches. We screened 60 compounds derived from A. indica bark and leaves through molecular docking to assess their binding affinity, followed by the evaluation of binding-free energies for the most promising candidates. Drug-likeness and pharmacokinetic properties were assessed, and molecular dynamics simulations were conducted to explore the conformational stability of the protein-ligand complexes. Our findings revealed that several A. indica compounds exhibited significantly lower docking scores (up to -9.669 kcal/mol) compared to the standard drug praziquantel (-4.349 kcal/mol). Notably, Isorhamnetin, Isomargolonone, Nimbaflavone, Quercetin, and Nimbionol demonstrated strong interactions with TGR, although Isorhamnetin showed potential mutagenicity. Further binding free energy calculations and molecular dynamics simulations confirmed the stability of Isomargolonone, Nimbionol, and Quercetin as potential TGR inhibitors. In conclusion, these findings suggest that Isomargolonone, Nimbionol, and Quercetin warrant further experimental validation as promising candidates for anti-schistosomal therapy.

Comprehensive Computational Screening and Analysis of Natural Compounds Reveals Promising Estrogen Receptor Alpha Inhibitors for Breast Cancer Therapy.

Breast cancer remains a leading cause of death among women, with estrogen receptor alpha (ERα) overexpression playing a pivotal role in tumor growth and progression. This study aimed to identify novel ERα inhibitors from a library of 561 natural compounds using computational techniques, including virtual screening, molecular docking, and molecular dynamics simulations. Four promising candidates-Protopine, Sanguinarine, Pseudocoptisine, and Stylopine-were selected based on their high binding affinities and interactions with key ERα residues. Molecular dynamics simulations conducted over 500 nanoseconds revealed that Protopine and Sanguinarine exhibited more excellent stability with minimal fluctuations, suggesting strong and stable binding. In contrast, Pseudocoptisine and Stylopine showed higher flexibility, indicating less stable interactions. Binding free energy calculations further supported the potential of Protopine and Sanguinarine as ERα inhibitors, though their binding strength was slightly lower than that of the reference compound. These findings highlight Protopine and Sanguinarine as leading candidates for further investigation, and in vitro and in vivo studies are recommended to evaluate their therapeutic potential in breast cancer treatment.

Identification of novel brain penetrant GSK-3β inhibitors toward Alzheimer’s disease therapy by virtual screening, molecular docking, dynamic simulation, and MMPBSA analysis

One of the significant therapeutic targets for Alzheimer’s disease (AD) is Glycogen Synthase Kinase-3β (GSK-3β). Inhibition of GSK-3β can prevent hyperphosphorylation of tau, and thus prevent formation and accumulation of neurofibrillary tangles and neuropil threads that block intracellular transport, trigger unfolded protein response, and increase oxidative stress, cumulatively leading to neurodegeneration. In this study, we have performed structure-based virtual screening of two small-molecule libraries from ChemDiv CNS databases using AutoDock Vina to identify hit molecules based on their binding affinities compared to that of an established GSK-3β inhibitor, indirubin-3’-monoxime (IMO). Pharmacoinformatic screening on SwissADME and pkCSM servers enabled identification of lead molecules with favorable pharmacoinformatic properties for drug likeliness, including blood brain barrier (BBB) permeability. Further, molecular dynamic simulations identified six candidate lead molecules that show stable complex formation with GSK-3β in dynamic state under physiological conditions. Principal component analysis of the dynamic state was used to plot Free Energy Landscapes (FELs) of GSK-3β-ligand complexes. STRIDE secondary structure analysis of the lowest energy conformations identified from FEL plots, and binding free energy calculations by Molecular Mechanics Poisson-Boltzmann Surface Area ((ΔGbind )MM-PBSA) of the simulation trajectories led to the identification of two ligands as potential lead molecules having favorable free energy landscape profiles as well as significantly lower (ΔGbind )MM-PBSA in dynamic state compared to that of reference inhibitor IMO. Hence, this study identifies two novel brain penetrant GSK-3β inhibitors that are likely to have therapeutic potential against Alzheimer’s disease.

Characterizing the Cooperative Effect of PROTAC Systems with End-Point Binding Free Energy Calculation.

Proteolytic targeting chimeras (PROTACs), as an emerging type of drug, function by proximity-based modalities that narrow the distance between a target protein and the E3 ubiquitin ligase to facilitate the ubiquitination labeling of the target protein for degradation. Although it is evidenced that the cooperativity of the PROTAC ternary interaction is one of the key factors affecting the degradation rate of a target protein, PROTAC design utilizing this indicator is still challenging because of the complicated/flexible interactions in a target-PROTAC-E3 ternary system. Therefore, developing reliable and practicable computational methods is of great interest for PROTAC design. Hence, in this study, we investigate the feasibility of using the end-point binding free energy calculation method, represented by molecular mechanics/Poisson-Boltzmann (generalized-Born) surface area (MM/PB(GB)SA), for characterizing cooperativity (including the stabilization and hook effects) of the PROTAC systems. The result shows that MM/GBSA is a good predictor in characterizing these effects under a relatively long molecular dynamics adjustment (50-100 ns) and low dielectric constant (εin = 1), with the Pearson correlation coefficient (rp) > 0.5 and 0.6 for the stabilization and hook effect, respectively. This study provides a feasible strategy for characterizing the cooperativity of the PROTAC systems, facilitating the rational design of PROTAC molecules.

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery.

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.