Calculated Binding Energies Research Articles

<b>In Silico Studies as Support for Natural Products Research</b>

It can be argued that in silico studies do not receive enough attention despite being a key part of addressing the limitations of our laboratory facilities, the high cost of chemicals, and the equipment required for wet laboratory activities. Natural product studies are demanding higher costs of chemicals, reagents, and varied laboratory facilities. This becomes a serious limitation in getting data from natural product studies. In silico studies use chemical structures as inputs as well as software and online web servers to generate data to support, predict, and validate wet laboratory activities. Interaction studies use computational tools to calculate binding energies and other associated properties. Predictions are based on the structure-activity relationships derived from previously conducted preclinical and clinical studies. As a main component of in silico studies, the physicochemical and pharmacokinetic properties of small molecules can be determined using online web servers such as SwissADME and ADMET web servers. An interaction study uses molecular docking software such as AutoDock, AutoDock vina, GOLD, and online servers such as SwissDock. Furthermore, the stabilities of complexes considered in interaction studies can be confirmed using molecular dynamics simulation software such as VMD. Prediction of activity spectra for substances (PASS) is widely used to predict biological activities for molecules based on MNA descriptors. In silico studies have played an important role in medicinal chemistry, pharmacology, and related research for screening, interaction studies, prediction, and other related purposes. Results of in silico predictions will not be far from wet lab activities as in most cases these studies consider previously attempted clinical and preclinical biological activities. Some examples are presented here to encourage the use of in silico studies. Received: 15 July 2024 | Revised: 27 August 2024 | Accepted: 25 Ocotber 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Fekade Beshah Tessema: Conceptualization, Methodology, Writing - original draft, Visualization. Tilahun Belayneh Asfaw: Validation, Writing - review & editing. Mesfin Getachew Tadesse: Resources, Visualization, Supervision. Yilma Hunde Gonfa: Validation, Writing - review & editing. Rakesh Kumar Bachheti: Resources, Visualization, Supervision.

Evaluating the estrogen degradation potential of laccase and peroxidase from Bacillus ligniniphilus L1 through integrated computational and experimental approaches

This study investigated the degradation potential of laccase and catalase-peroxidase from the extremophilic marine bacterium Bacillus ligniniphilus L1 against endogenous and synthetic estrogen compounds using an integrated computational and experimental approach. Molecular docking identified five estrogen compounds exhibiting reliable bindings with enzymes, which were then subjected to enzyme activity assays. The degradation potential of the two enzymes against five selected estrogen compounds were investigated and compared with their commercial counterparts. Laccase from L1 showed higher degradation potential against estrone (47.02 % without and 62.21 % with ABTS) compared to commercial laccase (39 % without and 54.20 % with ABTS). For estradiol valerate, commercial laccase showed a slightly higher degradation (52.47 %) than L1 laccase (49.94 %), but with ABTS, L1 laccase performed better (74.15 % vs. 68.03 %). Notably, L1 catalase-peroxidase demonstrated significantly higher degradation for all tested compounds compared to its commercial counterpart with efficiencies of 96.16 %, 89.09 %, 74.94 %, 64.91 %, and 62.80 % against estropipate, quinestrol, estradiol valerate, estriol and estrone, respectively, revealing its potential for commercial applications. Molecular dynamics simulations revealed the interaction and stability of enzyme-estrogen complexes, with MMGBSA binding energy calculations supporting experimental results. These findings highlight the usefulness of the computational approach in elucidating the molecular mechanisms underlying enzyme-mediated bioremediation of environmental contaminants.

Enhancing the specific activity of 3α-hydroxysteroid dehydrogenase through cross-regional combinatorial mutagenesis

3α-Hydroxysteroid dehydrogenase (3α-HSD) from Comamonas testosteroni is widely used in clinical settings to measure serum total bile acid levels. However, its low enzymatic activity leads to high operational costs. In this study, we employed a combinatorial mutagenesis approach to systematically identify potential key mutation sites within the enzyme. The enzyme molecule was segmented into distinct regions, and a comprehensive strategy integrating substrate pocket engineering, binding energy calculations, and deep learning techniques was used. Through experimental verification, single-point mutants from the mutation library with enhanced enzymatic activity by at least 1.5-fold were identified. Through iterative combinatorial mutations of them, the optimal mutant H119A/R201G/R216L was obtained. This mutant exhibited a specific activity of 34.18 U/mg towards deoxycholic acid, representing a 6.85-fold increase over the wild-type (WT) enzyme. Additionally, the optimal temperature of the mutant increased from 35 °C to 40 °C, and its turnover number and catalytic efficiency increased by 6.4-fold and 9.4-fold, respectively. Quantum mechanics/molecular mechanics (QM/MM) calculations indicated that the energy barrier of the dehydrogenase reaction was reduced in the H119A/R201G/R216L mutant compared to that of the WT enzyme. Specifically, the R201G mutation significantly reduced the electric field strength along the 3α-hydroxyl group, facilitating its deprotonation. This study provides insights into enhancing enzymatic efficiency through strategic mutagenesis and elucidates mechanistic changes that optimize enzyme performance for clinical and biotechnological applications.

Targeting SARS-CoV-2 Proteins: In Silico Investigation with Polypyridyl-Based Zn(II)-Curcumin Complexes.

Herein, we have selected eight Zn(II)-based complexes viz., [Zn(bpy)(acac)Cl] (1), [Zn(phen)(acac)Cl] (2), [Zn(dppz)(acac)Cl] (3), [Zn(dppn)(acac)Cl] (4), [Zn(bpy)(cur)Cl] (5), [Zn(phen)(cur)Cl] (6), [Zn(dppz)(cur)Cl] (7), [Zn(dppn)(cur)Cl] (8), where bpy=2,2'-bipyridine, phen=1,10-phenanthroline, dppz=benzo[i]dipyrido[3,2-a:2',3'-c]phenazine, dppn=naphtho[2,3-i]dipyrido[3,2-a:2',3'-c]phenazine, acac=acetylacetonate, cur=curcumin and performed in silico molecular docking studies with the viral proteins, i. e., spike protein (S), Angiotensin-converting enzyme II Receptor protein (ACE2), nucleocapsid protein (N), main protease protein (Mpro), and RNA-dependent RNA polymerase protein (RdRp) of SARS-CoV-2. The binding energy calculations, visualization of the docking orientation, and analysis of the interactions revealed that these complexes could be potential inhibitors of the viral proteins. Among complexes 1-8, complex 6 showed the strongest binding affinity with S and ACE2 proteins. 4 exerted better binding affinity in the case of the N protein, whereas 8 presented the highest binding affinities with Mpro and RdRp among all the complexes. Overall, the study indicated that Zn(II) complexes have the potential as alternative and viable therapeutic solutions for COVID-19.

An Extensive Computational Investigation of Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors from Diverse‐Lib Compounds Library

AbstractAntibiotics have played a crucial role in significantly reducing the incidence of tuberculosis (TB) infection worldwide. Even before the mid‐20th century, the mortality rate of TB onset within five years was around 50 %. So, the introduction of antibiotics has changed the scenario of TB from a serious threat to a manageable one. However, the emergence of resistance to anti‐TB drugs poses a significant challenge. So, to overcome this situation the therapeutic approaches and drug targets need to be reformed. This study focused on finding potential inhibitors by targeting Pantothenate Synthetase, a crucial enzyme for Mycobacterium tuberculosis (Mtb) survival, through computational drug discovery methods. Molecular docking and virtual screening were employed to identify potential inhibitors from Diverse‐lib. Four compounds, namely CID2813602, 24357538, CID753354, and CID4798023, exhibited strong binding energies and stable interaction with the target protein. Further assessment of these compounds through MD simulation and Post MD simulation showed significant dynamic stability. The minimum energy transition calculated using the free energy landscape analysis of these compounds when docked with Pantothenate Synthetase confirmed the stability of each complex due to its minimum energy production. The free binding energy calculation of each complex also showed the intramolecular interaction contributes to the strong binding affinity of the compounds within the enzyme's active site clarifying their mechanisms of action. This research showcases the effectiveness of computational methods in promptly identifying potential anti‐TB drugs, paving the way for future experimental validation and optimization. It holds promise for the development of new treatments targeting drug‐resistant TB strains.

Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules

Abstract We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spec- tra of organic molecules originating from the ionization energies of 1s electrons in carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed training dataset through high-throughput calculations of core-electron binding energies (CEBEs) for 12,880 small organic molecules in the bigQM7ω dataset, employing the ∆-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy using the ∆-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn–Sham eigenvalues. Upon application to 208 com- binatorially substituted uracil molecules, larger than those in the training set, our analyses reveal that while the models may not yield quantitatively accurate predictions of CEBEs on a molecule-by-molecule basis, they do exhibit a strong linear correlation, which proves valuable for virtual high-throughput screening purposes. We present the dataset and models as the Python module, cebeconf, to facilitate further explorations.

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.

Phytobioinformatics screening of ayurvedic plants for potential α-glucosidase inhibitors in diabetes management

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

Synthesis of Novel Indolyl Aryl Sulfone-clubbed Hydrazone Derivatives as Potential HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors: Molecular Modeling and QSAR Studies.

Non-Nucleoside Reverse Transcriptases Inhibitors (NNRTIs) are among the most extensively studied enzymes for understanding the biology of Human Immunodeficiency Viruses (HIV) and designing inhibitors for managing HIV infections. Indolyl aryl sulfones (IASs), an underexplored class of potent NNRTIs, require further exploration for the development of newer drugs for HIV. In this context, we synthesized a series of novels by Indolyl Aryl Sulfones with a hydrazone moiety at the carboxylate site of the indole nucleus. A 2D-QSAR model was developed to predict Reverse Transcriptase inhibitory activity against wild-type RT (WT-RT) enzyme. The model was successfully applied to predict the HIV-1 inhibitory activity of known Indolyl Aryl Sulfones. Considering the reliability, robustness, and reproducibility of the 2D-QSAR model, we made an in-silico prediction of the RT inhibition for our synthesized compounds (1-14). Molecular docking and dynamics simulations established our synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, as effective NNRTIs by stabilizing HIV reverse transcriptase's structure. Binding energy calculations revealed compound 28 as the strongest inhibitor (-43.21 ± 0.09 kcal/mol), followed by 23 (-40.94 ± 0.10 kcal/mol) and 24 (-39.18±0.08 kcal/mol), emphasizing their binding affinity towards HIV reverse transcriptase. In summary, the synthesized Indolyl Aryl Sulfones, particularly compounds 23, 24, and 28, demonstrate significant potential as Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) against HIV. These results highlight the promising role of these compounds in developing novel NNRTIs for managing HIV infections.

Quercetin: Potential antidiabetic effects through enzyme inhibition and starch digestibility

AbstractDiabetes mellitus involves high blood sugar levels due to insufficient insulin action. Furthermore, enzymes such as α‐amylase and α‐glucosidase break down carbohydrates into glucose, leading to postprandial hyperglycemia. Flavonoids, particularly quercetin, inhibit these enzymes, slowing carbohydrate digestion and reducing glucose absorption. Quercetin has significant hypoglycemic effects with inhibitory concentration (IC50) values comparable to acarbose, a standard inhibitor, suggesting its potential as a natural alternative for diabetes management. In silico models, including molecular docking, molecular dynamics (MD) simulations, and quantitative structure‐activity relationship (QSAR) approaches, help researchers understand the molecular interactions of therapeutic agents. These techniques identify potential inhibitors, determine enzyme‐inhibitor structures, and calculate binding energies, correlating findings with in vitro or in vivo data. Molecular docking predicts molecular orientations, MD simulations offer insights into enzyme–inhibitor dynamics, and QSAR models predict inhibitory potential based on structural properties. Studies have shown that quercetin effectively inhibits α‐glucosidase and α‐amylase by forming hydrogen bonds with specific amino acid residues. Quercetin interacts with starches and reduces their digestibility, increases the formation of resistant starch, lowers the glycemic index, and inhibits digestive enzymes. Studies show that the effects of quercetin on starch digestion vary with concentration and type of starch, and its incorporation into foods such as bakery products, pasta, etc. can significantly decrease starch hydrolysis. The incorporation of quercetin into starch matrices may aid in the development of functional foods aimed at improving glycemic control.

Computational Development of New Inhibitors for Apoptosis Stimulating Protein of p53 (iASPP) Using Pharmacophore Modeling, Docking, and MD Simulation Approaches

Inhibitor of apoptosis-stimulating protein of p53 (iASPP) overexpression is associated with diverse human tumors, including lung cancer, colorectal cancer, prostate cancer, acute leukemia, hepatocellular carcinoma, cervical cancer, and ovarian cancer. This study aims to find new and potent inhibitors for the iASPP drug target. Using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation an integrated strategy was developed to find highly effective iASPP inhibitors. Subsequently, the pharmacophore model was employed as a screening query to find promising inhibitors from the ZINC database. Total 36/12000 hits were identified against the iASPP drug target. The binding mode of the promising identified inhibitors was predicted by a molecular docking study. To evaluate the stability of the newly identified inhibitors top 03 best docking scores compounds were subjected to MD simulation. MD simulation and binding energy calculation confirmed that the compounds including ZINC001361049159, ZINC001361124195, and ZINC001545869876 were stable. The MD simulation analysis indicated that among all the compounds ZINC001361049159 was the most potent. These newly designed iASPP inhibitors could be used as starting material to identify new and potent anti-cancer drugs.

Distilling the Essential Elements of Nuclear Binding via Neural-Network Quantum States.

To distill the essential elements of nuclear binding, we seek the simplest Hamiltonian capable of modeling atomic nuclei with percent-level accuracy. A critical aspect of this endeavor consists of accurately solving the quantum many-body problem without incurring an exponential computing cost with the number of nucleons. We address this challenge by leveraging a variational MonteCarlo method based on a highly expressive neural-network quantum state ansatz. In addition to computing binding energies and charge radii of nuclei with up to A=20 nucleons, by evaluating their magnetic moments, we demonstrate that neural-network quantum states are able to correctly capture the self-emerging nuclear shell structure. To this end, we introduce a novel computational protocol based on adding an external magnetic field to the nuclear Hamiltonian, which allows the neural network to learn the preferred polarization of the nucleus within the given magnetic field.

Identification of Novel Tyrosinase Inhibitors with Nanomolar Potency Using Virtual Screening Approaches.

Hyperpigmentation disorders are caused by excess production of the pigment melanin, catalyzed by the enzyme tyrosinase. Novel tyrosinase inhibitors are needed as therapeutic agents to treat these conditions. To discover new inhibitors, we performed a virtual screening of the ZINC20 library containing 1.4 billion compounds. An initial filter for drug-likeness, ADMET properties, and synthetic accessibility reduced the library to 10,217 hits. Quantitative structure-activity relationship (QSAR) modeling of this subset predicted nanomolar inhibitory potency for several chemical scaffolds. Comparative molecular docking studies and rigorous binding energy calculations further prioritized four cysteine-containing dipeptide compounds based on predicted strong binding affinity and mode to tyrosinase. Microsecond-long molecular dynamics simulations provided additional atomistic insights into the stability of inhibitor-enzyme binding interactions. This integrated computational workflow effectively sampled an extremely large chemical space to discover four novel tyrosinase inhibitors with half-maximal inhibitory concentration values below 10 nM. Overall, this demonstrates the power of virtual screening and multi-faceted computational techniques to accelerate the discovery of potent bioactive ligands from massive compound libraries by efficiently sampling chemical space.

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.

Benzyl/phenyl‐1,2,3‐Triazole Tethered 3‐Acetyl Coumarins as Potential Drug‐Resistant Antitubercular Agents: Synthesis, Biology, and in Silico Investigations as Mtb DNA Gyrase Inhibitors

AbstractOwing to the emergence of multi‐drug resistant tuberculosis, there is a need for the exploration of new antitubercular agents. In this context, new coumarin‐based 1,2,3‐triazole hybrids were developed and evaluated for their antimicrobial activity against ESKAPE pathogens and the Mtb H37Rv strain. Among them, compounds 9 c and 12 showed MICs of 1 and 2 μg/mL, respectively, against the Mtb strain. The lead compounds exhibited a good selectivity index against Vero cells and were equally effective against ETB‐resistant and RIF‐resistant Mtb strains. Time‐kill kinetic studies revealed the bacteriostatic properties of the lead compounds, while combination studies using FDA‐approved antibiotics showed no drug interactions. Based on the structural similarity, it was envisaged that they might inhibit the DNA gyrase, which was further proved by the DNA supercoiling inhibition assay. Additionally, in silico docking studies, binding energy calculations, and ADME/T studies for the synthesized conjugates showed favourable pharmacokinetic and physicochemical characteristics. Hence, these molecules could further pave the way for discovering new potent antitubercular agents to combat AMR.

Unveiling neuroprotective mechanisms of diosgenin and pterostilbene in diabetes-associated Alzheimer’s disease through multi-target molecular docking approach

Introduction: Diabetes mellitus (DM) and Alzheimer’s disease (AD) are two highly linked disorders due to their association with the aging population. Several studies have reported the beneficial effects of diosgenin and pterostilbene in treating neurodegenerative diseases. This study aimed to investigate the neuroprotective mechanisms of diosgenin and pterostilbene through molecular docking and dynamics studies and assess their pharmacokinetic parameters. Methods: To understand the link between diabetes and AD, molecular docking of the natural ligands diosgenin and pterostilbene against the specific targets of AD, including β-secretase, glycogen synthetase kinase beta (GSK-3β), gamma-secretase, tumor necrosis factor-alpha (TNF-ɑ), and interleukin-6 (IL-6) was done to find out their binding affinities to explain the molecular mechanisms involved in their neuroprotection. Further molecular dynamics studies and binding energy calculations for the ligands with GSK-3β and β-secretase were carried out to confirm their docking activities. Additionally, pharmacokinetic analysis of these two phytoconstituents was performed by the SWISSADME server. Results: Molecular docking and dynamics studies revealed the good docking activity of diosgenin and pterostilbene with the selected targets. Further, the two phytoconstituents revealed suitable pharmacokinetic parameters along with blood-brain barrier permeability, confirming their druggable nature. Conclusion: This research identified multiple neuroprotective pathways of diosgenin and pterostilbene that might be significant for treating diabetes-associated Alzheimer’s disease.

Assessing pristine and metal doped C2N monolayer as a nanocarriers for anticancer drug

Theoretical research has introduced two-dimensional structures of thin sheets known as the pristine C2N monolayer and the Zn-doped C2N monolayer. These sheets show promise as nanocarriers for delivering the anticancer drug purinethol (PU). Through calculations of binding energy (Eb), it was observed that both the pristine C2N monolayer (−0.505 eV) and the Zn-decorated C2N monolayer (−0.762 eV) exhibit favorable characteristics for drug delivery. Eb values fall within range of physisorption, indicating their suitability as candidates for transporting drugs. An observed charge transfer (CT) of 0.035 e occurs in the Zn-decorated C2N monolayer, leading to a depletion of charge in the Zn-doped C2N monolayer system. The primary contributor to this charge loss is the Zn atom, which experiences a charge reduction of 0.035 e. To understand the phenomenon of drug release, the binding energy was recalculated under biological conditions, specifically in an acidic environment. The results indicate a decline in Eb (−0.218 eV) as well as a short recovery time, suggesting successful release of PU within body. The theoretical predictions we have made are expected to serve as inspiration for experimental researchers in their efforts to design drug delivery systems (DDSs) based on C2N monolayers.

Computational Analysis and Inhibition Study of Carbonic Anhydrase from Azadirachta indica Leaves

Introduction: Carbonic anhydrase (CA) has been an enzyme of great interest for its application in carbon dioxide sequestration. A total of 66 protein sequences of plant CAs were computationally analyzed and submitted to bioinformatics for motif and domain identification, multiple sequence alignment, and phylogenetic analysis. Method: The current work aims to extend knowledge among researchers in better understanding the structure of AZDI CA by analyzing its physicochemical properties, secondary structure prediction, 3D modeling of protein sequence, and its validation using a variety of conventional computational methods. Results: The accuracy of the predicted 3D structure checked by the Ramachandran plot generated by PROCHECK showed that for the CA protein sequence 93.1%, was observed in the most favored regions, VERIFY 3D 69.44% and ERRAT 98.09%. AZDI CA was docked with various anions and sulphonamide derivatives to study their inhibitory effect and calculate binding energy by using Autodock 4.2. Ethoxzolamide is found to be a better inhibitor of AZDI CA with binding energy -8.71 kCal/mol and KI value 0.0004 mM. Further MD simulation of the docked complex of ethoxzolamide with AZDI CA was done using SCHRODINGER DESMOND software. Conclusion: The outcomes of this research will have a significant influence on biochemistry, biotechnology, and potential applications of AZDI CA and similar plant CAs in determining potent inhibitors for drug targets in treating various diseases.

A Study on the Binding Mechanism and the Impact of Key Residue Mutations between SND1 and MTDH Peptide through Molecular Dynamics Simulations.

Metastasis of breast cancer is the main cause of death for patients with breast cancer. The interaction between metadherin (MTDH) and staphylococcal nuclease domain 1 (SND1) plays a pivotal role in promoting breast cancer development. However, the binding details between MTDH and SND1 remain unclear. In this study, we employed all-atom molecular dynamics simulations (MDs) and conducted binding energy calculations to investigate the binding details and the impact of key residue mutations on binding. The mutations in key residues have not significantly affected the overall stability of the structure and the fluctuation of residues near the binding site; they have exerted a substantial impact on the binding of SND1 and MTDH peptide. The electrostatic interactions and van der Waals interactions play an important role in the binding of SND1 and the MTDH peptide. The mutations in the key residues have a significant impact on electrostatic and van der Waals interactions, resulting in weakened binding. The energy contributions of key residues mainly come from the electrostatic energy and van der Waals interactions of the side chain. In addition, the key residues form an intricate and stable network of hydrogen bonds and salt-bridge interactions with the MTDH peptide. The mutations in key residues have directly disrupt the interactions formed between SND1 and MTDH peptide, consequently leading to changes in the binding mode of the MTDH peptide. These analyses unveil the detailed atomic-level interaction mechanism between SND1 and the MTDH peptide, providing a molecular foundation for the development of antibreast cancer drugs.

Discovery of Potential Inhibitors of CDK1 by Integrating Pharmacophore-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation Studies, and Evaluation of Their Inhibitory Activity.

The ability of CDK1 to compensate for the absence of other cell cycle CDKs poses a great challenge to treat cancers that overexpress these proteins. Despite several studies focusing on the area, there are no FDA-approved drugs selectively targeting CDK1. Here, the study aimed to develop potential CDK1 selective inhibitors through drug repurposing and leveraging the structural insights provided by the hit molecules generated. Approximately 280,000 compounds from DrugBank, Selleckchem, Otava and an in-house library were screened initially based on fit values using 3D QSAR pharmacophores built for CDK1 and subsequently through Lipinski, ADMET, and TOPKAT filters. 10,310 hits were investigated for docking into the binding site of CDK1 determined using the crystal structure of human CDK1 in complex with NU6102. The best 55 hits with better docking scores were further analyzed, and 12 hits were selected for 100 ns MD simulations followed by binding energy calculations using the MM-PBSA method. Finally, 10 hit molecules were tested in an in vitro CDK1 Kinase inhibition assay. Out of these, 3 hits showed significant CDK1 inhibitory potential with IC50 < 5 μM. These results indicate these compounds can be used to develop subtype-selective CDK1 inhibitors with better efficacy and reduced toxicities in the future.