Free Energy Landscape Analysis Research Articles

Identification of novel PAD2 inhibitors using pharmacophore-based virtual screening, molecular docking, and MD simulation studies.

In the realm of epigenetic regulation, Protein arginine deiminase 2 (PAD2) stands out as a therapeutic target due to its significant role in neurological disorders, rheumatoid arthritis (RA), multiple sclerosis (MS), and various cancers. To date, no in silico studies have focused on PAD2 for lead compound identification. Therefore, we conducted structure-based pharmacophore modeling, virtual screening, molecular docking, molecular dynamics (MD) simulations, and essential dynamics studies using PCA and free energy landscape analyses to identify repurposed drugs and selective inhibitors against PAD2. The best pharmacophore model, 'Pharm_01,' had a selectivity score of 10.485 and an excellent ROC curve quality of 0.972. Pharm1 consisted of three hydrogen bond donors (HBD) and two hydrophobic (Hy) features (DDDHH). A virtual screening of about 9.2million compounds yielded 2575 hits using a fit value threshold of 2.5 and drug-likeness criteria. Molecular docking identified the top ten molecules, which were verified using MD simulations. Stability was verified using MM-PBSA studies, whereas conformational differences were investigated using PCA and free energy landscape analyses. Two hits (Leads 1 and 2) from the DrugBank dataset showed promise for repurposing as PAD2 inhibitors, while one hit compound (Lead 8) from the ZINC database emerged as a novel PAD2 inhibitor. These findings indicate that the discovered compounds may be potent PAD2 inhibitors, necessitating additional preclinical and clinical research to produce viable treatments for cancer and neurological disorders.

Identification of Potential Inhibitors of TGFβR1 for the Treatment of Cancer through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

Globally, cancer is one of the leading causes of death. Resistance to conventional medications, such as chemotherapy and radiation, continues to be a significant challenge in the treatment of cancer despite the availability of numerous medicines. Therefore, the highest priority is to hunt for new therapeutic agents. Transforming growth factor-beta is a pivotal regulatory cytokine that exerts significant influence over cellular processes, particularly emphasizing its role in facilitating and modulating cell proliferation. TGF-β receptor 1, identified as the most promising active site of the TGF-β signaling, is a potent drug target site that has garnered wide attention for developing new anticancer agents. The present investigation investigates the potential natural products as TGFβR1 inhibitors. The SB431542 complexed TGFβR1 protein model was used to screen the natural product database to obtain a compound with high binding potential. NPC247629 has emerged as the best-scored compound among all the screened compounds, demonstrating the highest affinity towards the TGFβR1 regarding docking score -17.54 kcal/mol. The all-atoms MD simulation study indicated that all proposed hits are retained inside the receptor in dynamic states. Additionally, principal component and free energy landscape analysis were performed to explore the binding mechanism of top-hit natural products. The best-screened hits, NPC247629 and NPC60735, have excellent binding affinity and hold a massive potential for TGFβR1 inhibition, paving the way for promising future investigations in cancer treatment.

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.

Structural and functional consequences of non-synonymous SNPs within the LAMA2 protein: a molecular dynamics perspective

Clinical phenotypic presentations associated with LAMA2 deficiency have shown a variety of manifestations. LAMA2 mutations are mainly linked to congenital muscular dystrophy, but there is also mounting evidence suggesting their presence in inflammatory breast cancer, laryngopharyngeal squamous cell carcinoma, and ventricular tachycardia related to coronary artery disease and cardiomyopathy. This study examined the structural and functional impacts of 144 non-synonymous single nucleotide polymorphisms (nsSNPs) within the LAMA2 gene. Through multi-tiered sequence and structure-based methods, 11 deleterious and destabilizing mutations were identified (A1362T, E1308Q, E1360G, I1276S, L1195P, M1359T, P1232H, P1238A, P1272L, Y1234H, Y1338C). Further, four mutations (L1195P, Y1234H, P1238A, A1362T), which aligned with conserved positions, were subjected to 500 ns molecular dynamics (MD) simulations. RMSD calculated from MD trajectories highlighted structural disparities between wild-type and mutant forms, with the latter showing greater flexibility. Radius of gyration analysis indicated reduced compactness, solvent accessibility changes suggested unfolding, and hydrogen bond (HB) analysis demonstrated disrupted integrity. The HB analysis revealed disruptions in structural integrity due to diminished hydrogen bonds in mutants. Secondary structure analysis revealed significant alterations in secondary structural content. Principal Component Analysis unveiled increased dynamic behavior in mutants. Gibbs free energy landscape analysis reflected distinct energy minima regions in mutants, indicating structural destabilization. Overall, this study revealed the functional and structural ramifications of nsSNPs in the LAMA2 gene, providing valuable insights into potential disease-causing mutations and warranting future research on understanding LAMA2 associated diseases and disorders.

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.

Structure of G protein-coupled receptor GPR1 bound to full-length chemerin adipokine reveals a chemokine-like reverse binding mode.

Chemerin is an adipokine with chemotactic activity to a subset of leukocytes. Chemerin binds to 3 G protein-coupled receptors, including chemokine-like receptor 1 (CMKLR1), G protein-coupled receptor 1 (GPR1), and C-C chemokine receptor-like 2 (CCRL2). Here, we report that GPR1 is capable of Gi signaling when stimulated with full-length chemerin or its C-terminal nonapeptide (C9, YFPGQFAFS). We present high-resolution cryo-EM structures of Gi-coupled GPR1 bound to full-length chemerin and to the C9 peptide, respectively. C9 insertion into the transmembrane (TM) binding pocket is both necessary and sufficient for GPR1 signaling, whereas the full-length chemerin uses its bulky N-terminal core for interaction with a β-strand located at the N-terminus of GPR1. This interaction involves multiple β-strands of full-length chemerin, forming a β-sheet that serves as a "lid" for the TM binding pocket and is energetically expensive to remove as indicated by molecular dynamics simulations with free energy landscape analysis. Combining results from functional assays, our structural model explains why C9 is an activating peptide at GPR1 and how the full-length chemerin uses a "two-site" model for enhanced interaction with GPR1.

Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility.

Deciphering the multi-scale mechanism of herbal phytoconstituents in targeting breast cancer: a computational pharmacological perspective

Breast Cancer (BC) is the most common cause of cancer-associated deaths in females worldwide. Despite advancements in BC treatment driven by extensive characterization of its molecular hallmarks, challenges such as drug resistance, tumor relapse, and metastasis persist. Therefore, there is an urgent need for alternative treatment approaches with multi-modal efficacy to overcome these hurdles. In this context, natural bioactives are increasingly recognized for their pivotal role as anti-cancer compounds. This study focuses on predicting molecular targets for key herbal phytoconstituents—gallic acid, piperine, quercetin, resveratrol, and beta-sitosterol—present in the polyherbal formulation, Krishnadi Churna. Using an in-silico network pharmacology model, key genes were identified and docked against these marker compounds and controls. Mammary carcinoma emerged as the most significant phenotype of the putative targets. Analysis of an online database revealed that out of 135 predicted targets, 134 were mutated in breast cancer patients. Notably, ESR1, CYP19A1, and EGFR were identified as key genes which are known to regulate the BC progression. Docking studies demonstrated that the herbal phytoconstituents had similar or better docking scores than positive controls for these key genes, with convincing protein-ligand interactions confirmed by molecular dynamics simulations, MM/GBSA and free energy landscape (FEL) analysis. Overall, this study highlights the predictive potential of herbal phytoconstituents in targeting BC genes, suggesting their promise as a basis for developing new therapeutic formulations for BC.

Comprehensive in silico characterization of Arabidopsis thaliana RecQl helicases through structure prediction and molecular dynamics simulations

Helicases are ubiquitous enzymes with specific functions that contribute to almost all nucleic acid metabolic processes. The RecQ helicase family is essential for integrity in all organisms through DNA replication, repair, and recombination. This study investigated five RecQ-like helicases in Arabidopsis thaliana (AtRecQl) that exhibit diverse structural and physiochemical attributes and functions. Cis-regulatory element analysis identified stress, hormone, cell cycle, and development-responsive modules involved in various events in plant growth and development. Gene ontology analysis revealed that the five AtRecQl were associated with various cellular components, molecular functions, and biological processes. Protein-protein interaction analysis also implicated some in various abiotic stress processes. Structural analysis and molecular dynamics (MD) simulations were performed to examine conformational stability through root means square deviation and radius of gyration, showing stable AtRecQl protein structures. Free energy landscape analysis validated thermodynamically stable structures throughout the MD simulation. Principle component analysis and probability density functions from MD simulations provided satisfactory structural variational data for the complexes and limited coordinate movements. These insights might greatly benefit future studies.

Exploring Phytochemical Compounds Against Pseudomonas Aeruginosa Using QSAR, Molecular Dynamics, and Free Energy Landscape

AbstractPseudomonas aeruginosa is a versatile opportunistic bacterium that presents a considerable risk in medical environments because of its strong adaptability and resistance to multiple medications. Targeting the LasR quorum sensing system, which plays a crucial role in controlling virulence factors and biofilm formation, is a key intervention point. In this study, in silico molecular docking, machine learning‐based Quantitative Structure‐Activity Relationship (QSAR) techniques along with molecular dynamics simulation were employed to screen phytochemical compounds for their ability to inhibit the LasR QS system, a key regulator of virulence in Pseudomonas aeruginosa. This study screened 1652 phytochemicals using the ML‐based QSAR model to identify 52 phytochemicals that had better activity than the control (N‐{[3,5‐dibromo‐2‐(methoxymethoxy)phenyl]methyl}‐2‐nitrobenzamide). The in silico molecular docking approach that targeted LasR identified compounds 5281647, 57331045, and 5281672 with high binding affinity and hydrogen bonds that were comparable to the control (docking score=−10.3 kcal/mol and hydrogen bonds=4). In the 200 ns post‐molecular dynamics simulation, 5281647 exhibited a stable RMSD of 0.25 nm, which was comparable to the control. The maximum number of hydrogen bonds was exhibited by 57331045, while 5281647 and 5281672 consistently exhibited four hydrogen bonds. Overall, the Principal component analysis (PCA) and Free Energy Landscape (FEL) analyses of the complexes demonstrated that these three compounds were in stable states. In comparison to the control (ΔGTOTAL=−39.58 kcal/mol), the cumulative binding free energy (ΔGTOTAL) for 5281647 and 57331045 was −39.95 kcal/mol and −39.25 kcal/mol, respectively. This further confirms the superior binding affinity of the two compounds. Both 5281647 and 57331045 were identified as potent inhibitors of the LasR transcription factor, which is essential for the quorum sensing of Pseudomonas aeruginosa, in the present investigation. These findings underscore the importance of further exploration and optimization of phytochemicals for combating bacterial infections, offering promising avenues for future drug discovery efforts targeting this resilient pathogen.

Integrated all-atom and coarse-grained simulations uncover structural, dynamics and energetic shifts in SARS-CoV-2 JN.1 and BA.2.86 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the COVID-19 pandemic, is an enveloped, positive-stranded RNA virus that enters human cells by using its spike protein to bind to the human angiotensin-converting enzyme 2 (ACE2) receptor. Since its emergence, the virus has mutated, producing variants with increased transmissibility, immune evasion, and infectivity. The JN.1 variant, detected in January 2024, features a single substitution mutation (Leu455Ser) in the receptor-binding domain (RBD) of its spike protein, setting it apart from its parent lineage, BA.2.86. This variant has rapidly become globally predominant due to its enhanced transmission and significant epidemiological impact. To understand the causes behind the dominance of the JN.1 variant, we conducted a comprehensive study using all-atom molecular dynamics (MD) and coarse-grained MD simulations. This allowed us to examine the structural, dynamic, energetics and binding properties of the wild-type (Wuhan strain), BA.2.86, and JN.1 variants. Principal component and free energy landscape analyses revealed enhanced structural stability in the JN.1 variant. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) assessments indicated lower binding affinity for JN.1 as compared to BA.2.86. Intermolecular interaction analyses further confirmed BA.2.86′s superior binding affinity over JN.1 and wild-type. Additionally, we compared and validated our findings against experimentally determined cryo-electron microscopy (cryo-EM) structures of JN.1 and BA.2.86 variants, confirming the reliability of our simulation results. Overall, this study provides crucial insights into the structural-dynamics-energetics features and physicochemical properties that have contributed to the global prevalence of the JN.1 variant and sheds light on its potential to generate future subvariants.

Harnessing marine natural products to inhibit PAD4 triple mutant: A structure-based virtual screening approach for rheumatoid arthritis therapy

Peptidylarginine deiminase type4 (PAD4) is a pivotal pro-inflammatory protein within the human immune system, intricately involved in both inflammatory processes and immune responses. Its role extends to the generation of diverse immune cell types, including T cells, B cells, natural killer cells, and dendritic cells. PAD4 has recently garnered attention due to its association with a spectrum of inflammatory and autoimmune disorders, notably rheumatoid arthritis (RA). Mutations in the PAD4 gene, leading to the conversion of arginine to citrulline, have emerged as significant factors in the pathogenesis of RA and related conditions. As a calcium-dependent enzyme, PAD4 is central to the citrullination process, a crucial post-translational modification implicated in disease pathophysiology. Its critical role in autoimmune disorders and inflammation makes PAD4 a prime candidate for therapeutic intervention in RA. Inhibiting PAD4 presents a promising avenue for mitigating inflammatory responses and curtailing joint degradation and impairment. To explore its therapeutic potential, a structure-based virtual screening (SBVS) approach was employed, harnessing an array of marine natural products (MNPs) sourced from databases such as CMNPD, MNPD, and Seaweed. Notably, MNPD10752, CMNPD12680, and CMNPD2751 emerged as potential hit molecules, exhibiting adherence to essential pharmacokinetic properties and favorable toxicity profiles. Quantum mechanics studies using density functional theory (DFT) calculations revealed the inhibitory potential of these identified natural products. Further structural elucidation through molecular dynamics simulations (MDS) and principal component-based free energy landscape (FEL) analysis shed light on the stability of MNP-bound PAD4 complexes. In conclusion, this computational study serves as a stepping stone for further experimental evaluation, aiming to explore the potential of MNPs in addressing PAD4-related human pathologies.

Unveiling therapeutic biomarkers and druggable targets in ALS: An integrative microarray analysis, molecular docking, and structural dynamic studies

Amyotrophic lateral sclerosis (ALS), commonly known as Lou Gehrig's disease, is a debilitating neurodegenerative disorder characterized by the progressive degeneration of nerve cells in the brain and spinal cord. Despite extensive research, its precise etiology remains elusive, and early diagnosis is challenging due to the absence of specific tests. This study aimed to identify potential blood-based biomarkers for early ALS detection and monitoring using datasets from whole blood samples (GSE112680) and oligodendrocytes, astrocytes, and fibroblasts (GSE87385) obtained from the NCBI-GEO repository. Through bioinformatics analysis, including protein-protein interactions and molecular pathway analyses, we identified differentially expressed genes (DEGs) associated with ALS. Notably, ALS2, ADH7, ALDH8A1, ALDH3B1, ABHD2, ABHD17B, ABHD12, ABHD13, PGAM2, AURKB, ANAPC11, VAPA, UNC45B, and TNNT2 emerged as top-ranked DEGs, implicated in drug metabolism, protein depalmytilation, and the AKT/mTOR signaling pathways. Among these, AurKB established as a potential therapeutic biomarker with relevance to various neurological conditions. Consequently, AurKB was selected for identifying potential therapeutic molecules and utilized for in silico structural characterization studies. Exploration of the IMPATT database led to the discovery of a lead compound similar to Fostamatinib, currently used for AurKB. Initial molecular docking and MMGBSA-based binding energy analysis were followed by molecular dynamics simulation (MDS) and free energy landscape (FEL) analysis to validate the ligand's binding efficacy and understand dynamic processes within the biological system. The identified potential biomarkers and lead molecule provide novel insights into the correlation between blood cell transcripts and ALS pathology, paving the way for blood-based diagnostic tools for early ALS detection and ongoing disease monitoring.

Identification of potential NUDT5 inhibitors from marine bacterial natural compounds via molecular dynamics and free energy landscape analysis.

NUDIX hydrolase 5 (NUDT5) is an enzyme involved in the hydrolysis of nucleoside diphosphates linked to other moieties, such as ADP-ribose. This cofactor is vital in redox reactions and is essential for the activity of sirtuins and poly(ADP-ribose) polymerases, which are involved in DNA repair and genomic stability. It has been shown that NUDT5 activity can also influence NAD+ homeostasis, thereby affecting cancer cell metabolism and survival. In this regard, the discovery of NUDT5 inhibitors has emerged as a potential therapeutic approach in cancer treatment. In this study, we conducted a high-throughput virtual screening of marine bacterial compounds against the NUDT5 enzyme and four molecules were selected based on their docking scores. These compounds established strong interactions within the NUDT5 active site, with molecular analysis highlighting the key role of Trp28A and Trp46B residues. Molecular dynamics simulations over 200 ns indicated a stable behavior, in association with root mean square deviation values always below 3 Å, suggesting conformational stability. Free energy landscape analysis further supported their potential as NUDT5 inhibitors, offering avenues for novel therapeutic strategies against NUDT5-associated breast cancer.

Suppressing Mycobacterium tuberculosis virulence and drug resistance by targeting Eis protein through computational drug discovery.

Tuberculosis (TB) remains a critical health threat, particularly with the emergence of multidrug-resistant strains. This demands attention from scientific communities and healthcare professionals worldwide to develop effective treatments. The enhanced intracellular survival (Eis) protein is an acetyltransferase enzyme of Mycobacterium tuberculosis that functions by adding acetyl groups to aminoglycoside antibiotics, which interferes with their ability to bind to the bacterial ribosome, thereby preventing them from inhibiting protein synthesis and killing the bacterium. Therefore, targeting this protein accelerates the chance of restoring the aminoglycoside drug activity, thereby reducing the emergence of drug-resistant TB. For this, we have screened 406,747 natural compounds from the Coconut database against Eis protein. Based on MM/GBSA rescoring binding energy, the top 5 most prominent natural compounds, viz. CNP0187003 (-96.14kcal/mol), CNP0176690 (-93.79kcal/mol), CNP0136537 (-92.31kcal/mol), CNP0398701 (-91.96kcal/mol), and CNP0043390 (-91.60kcal/mol) were selected. These compounds exhibited the presence of a substantial number of hydrogen bonds and other significant interactions confirming their strong binding affinity with the Eis protein during the docking process. Subsequently, the MD simulation of these compounds exhibited that the Eis-CNP0043390 complex was the most stable, followed by Eis-CNP0187003 and Eis-CNP0176690 complex, further verified by binding free energy calculation, principal component analysis (PCA), and Free energy landscape analysis. These compounds demonstrated the most favourable results in all parameters utilised for this investigation and may have the potential to inhibit the Eis protein. There these findings will leverage computational techniques to identify and develop a natural compound inhibitor as an alternative for drug-resistant TB.

Role of interaction mode of phenanthrene derivatives as selective PDE5 inhibitors using molecular dynamics simulations and quantum chemical calculations.

Phosphodiesterase type 5 (PDE5) inhibitors play a crucial role in blocking PDE5 to improve erectile dysfunction (ED). However, most PDE5 drugs revealed side effects including the loss of vision due to the PDE6 inhibition. Phenanthrene derivatives isolated from E. macrobulbon were previously reported as PDE5 inhibitors. Two phenanthrene derivatives (cpds 1-2) revealed better inhibition to PDE5 than PDE6 and cpd 1 is more selective to PDE5 than cpd 2. To elucidate why the phenanthrene derivatives could inhibit PDE5 and PDE6, their binding modes were investigated using molecular dynamics simulations and quantum chemical calculations, as compared to the PDE5 drugs. From the results, all four drugs and phenanthrene derivatives revealed similar π-π interactions to Phe820 in PDE5. Additional H-bond interaction to Gln817 in PDE5 resulted in better PDE5 inhibition of vardenafil and tadalafil. Moreover, cpds 1-2 were able to form the H-bond interaction with Asp764 in PDE5. In the case of the PDE6, the loss of π-π interaction to Phe776 and H-bond interaction to Gln773 indicated the important points for losing the PDE6 inhibition. In conclusion, to develop the new potent PDE5 inhibitors, not only the important interaction with PDE5 but also the interaction with PDE6 should be considered. In phenanthrene derivatives, the middle ring was significant to form π-π interactions to Phe820 in PDE5 and hydroxyl substituent was also the key part to form the H-bond interaction with Asp764 in PDE5. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated the stability of the system. The bioavailability, drug-likeness, and pharmacokinetics of phenanthrene derivatives were also predicted. These derivatives revealed good drug-likeness and GI absorption. The obtained results showed that phenanthrene derivatives could be interesting for the development of PDE5 inhibitors in the future.

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the β-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

Exploring Plant-Based Compounds as Alternatives for Targeting Enterococcus faecalis in Endodontic Therapy: A Molecular Docking Approach.

Endodontic infections pose significant challenges in dental practice due to their persistence and potential complications. Among the causative agents, Enterococcus faecalis stands out for its ability to form biofilms and develop resistance to conventional antibiotics, leading to treatment failures and recurrent infections. The urgent need for alternative treatments arises from the growing concern over antibiotic resistance and the limitations of current therapeutic options in combating E. faecalis-associated endodontic infections. Plant-based natural compounds offer a promising avenue for exploration, given their diverse bioactive properties and potential as sources of novel antimicrobial agents. In this study, molecular docking and dynamics simulations are employed to explore the interactions between SrtA, a key enzyme in E. faecalis, and plant-based natural compounds. Analysis of phytocompounds through molecular docking unveiled several candidates with binding energies surpassing that of the control drug, ampicillin, with pinocembrin emerging as the lead compound due to its strong interactions with key residues of SrtA. Comparative analysis with ampicillin underscored varying degrees of structural similarity among the study compounds. Molecular dynamics simulations provided deeper insights into the dynamic behavior and stability of protein-ligand complexes, with pinocembrin demonstrating minimal conformational changes and effective stabilization of the N-terminal region. Free energy landscape analysis supported pinocembrin's stabilizing effects, further corroborated by hydrogen bond analysis. Additionally, physicochemical properties analysis highlighted the drug-likeness of pinocembrin and glabridin. Overall, this study elucidates the potential anti-bacterial properties of selected phytocompounds against E. faecalis infections, with pinocembrin emerging as a promising lead compound for further drug development efforts, offering new avenues for combating bacterial infections and advancing therapeutic interventions in endodontic practice.

High throughput virtual screening and validation of Plant-Based EGFR L858R kinase inhibitors against Non-Small cell lung Cancer: An integrated approach Utilizing GC–MS, network Pharmacology, Docking, and molecular dynamics

Lung cancer ranks as the 2nd most common cancer globally. It’s the most prevalent cancer in men and the 2nd most common in women. The prominent events in EGFR-mutated non-small-cell lung cancer (NSCLC) include the emergence of the L858R mutation within EGFR exon 21. Despite the promising efficacy of EGFR inhibitors in managing lung cancer, the development of acquired resistance poses a significant hurdle. In the current investigation, we focused on the screening of two phytochemicals, namely Dehydrocostus lactone and Mokkolactone, derived from the Saussurea lappa plant, as potential inhibitors targeting EGFR L858R mutant lung cancer. The chloroform and ethanol extract of the plant demonstrated anti-proliferative activity through the Resazurin chemosensitivity assay, exhibiting an IC50 value of 37.90 ± 0.29 µg/ml with selectivity index 2.4. Through a GC–MS study, we identified 11 phytochemicals for further insilico analysis. These compounds underwent ADMET assessment followed by drug likeliness analysis before being subjected to molecular docking against EGFR L858R, identified through protein–protein interaction network analysis. All phytochemicals exhibited binding energy scores ranging from −6.9 to −8.1 kcal/mol. Dehydrocostus lactone and Mokkolactone were specifically identified for their binding profile. Findings from 100 ns molecular dynamics simulations demonstrated their enhanced stability compared to the reference ligand DJK. This was evident in the root mean square deviation (RMSD) values, ranging from 0.23 ± 0.01 nm to 0.30 ± 0.05 nm, the radius of gyration values, from 1.71 ± 0.01 nm to 1.72 ± 0.01 nm, and the solvent accessible surface area values, from 155.39 ± 2.40 nm2 to 159.32 ± 2.14 nm2. Additionally, favourable characteristics were observed in terms of hydrogen bonding, principal component analysis, and free energy landscape analysis. Examination of their electronic structure via density functional theory revealed efficient properties, with the highest occupied molecular orbital-least unoccupied molecular orbital energy gap values ranging from −3.984 eV to −6.547 eV. Further, in vivo analysis is required to gain a more comprehensive understanding and efficacy of these identified phytochemicals against lung cancer.

Exploring KRas Protein Dynamics: An Integrated Molecular Dynamics Analysis of KRas Wild and Mutant Variants.

This study employs a comprehensive approach combining protein retrieval, sequence alignment, and molecular dynamics simulations to investigate the structural dynamics and stability of wild-type KRas and its mutated variants (G12C, G12D, G12V, and G13D). The selected protein structures were retrieved from the Protein Data Bank (PDB) and prepared by using visual molecular dynamics (VMD) software. Sequence alignment using Clustal Omega provided a detailed comparison of the amino acid sequences, focusing on key mutation sites. Molecular dynamics simulations, performed with Gromacs, revealed distinct conformational changes and stability patterns in the wild-type and mutated KRas proteins over 100 ns. Clustering analysis identified higher conformational changes in the second α-helix of the mutated variants. The root-mean-square deviation (RMSD) distribution analysis showed variant-specific conformational dynamics, with G12V and G12D exhibiting slightly higher average RMSD values. Furthermore, clustering and RMSD analyses of specific amino acid residues (12, 13, 51, and 118) highlighted their roles in maintaining overall stability and influencing structural dynamics. The results indicate that mutations at positions 12 and 13 disrupt normal cycling between wild and mutated variants, leading to the persistent activation of KRas. Additionally, principal component analysis (PCA) elucidated unique conformational dynamics in mutated variants. Free energy landscape (FEL) analysis revealed alterations in the thermodynamic stability of mutated variants compared with the wild type. Overall, this study provides a detailed understanding of the structural changes associated with oncogenic mutations in KRas, offering insights crucial for targeted therapeutic strategies in KRas-driven cancers.