Benzimidazole scaffolds are widely recognized as important structural motifs in medicinal chemistry; however, their potential to function as dual-acting agents against both oxidative stress and parasitic infections have not been extensively explored. In the present study, two novel benzimidazole derivatives a thiazine-conjugated analogue (Thz-BZIM) and an N-tosylated analogue (Ts-BZIM) were examined. Quantum chemical calculations suggest that these compounds possess notable radical-scavenging capability, primarily through favorable electron and proton transfer mechanisms. This behavior is further supported by thermochemical parameters and electronic structure descriptors that indicate good redox stability. Molecular docking studies reveal distinct target selectivity: Thz-BZIM preferentially binds to human 11β-hydroxysteroid dehydrogenase type 1 (HSD1, PDB ID: 2IRW), a regulator of metabolic inflammation; while Ts-BZIM shows favorable interactions with Leishmania major glycogen synthase kinase-3α (LmGSK-3α, PDB ID: 3E3P), which is essential for parasite survival. Furthermore, molecular dynamics simulations and pharmacophore modeling demonstrate the stability of the protein-ligand complexes and reveal consistent binding modes within the respective target proteins. ADMET evaluation also indicates that both derivatives possess favorable drug-like characteristics. Collectively, these results underscore the multifunctional potential of benzimidazole-based antioxidants, suggesting their ability to interact with different protein targets while preserving their antioxidant properties. Although further experimental validation is required, this in silico study provides valuable insights into protein-ligand interactions and establishes a rational foundation for the development of multifunctional therapeutics with potential relevance to metabolic disorders and parasitic infections.

HIV drug resistance continues to undermine the long-term efficacy of antiretroviral therapy and sustains the demand for new antiviral chemotypes and alternative target hypotheses. In this work, 2-benzothiazolyl sulfide (bis(benzo[d]thiazol-2-yl)sulfane, 4) was synthesized, characterized by NMR, IR, MS and single-crystal X-ray diffraction, and examined as a benzothiazole-based scaffold using an in silico workflow. Structure-based docking was performed against two HIV-1 protein pockets defined by co-crystallized ligands: the envelope glycoprotein gp120 CD4-binding-site cavity (PDB: 4DVR) and the capsid N-terminal-domain inhibitor pocket (PDB: 4INB). Redocking of the native ligands reproduced the crystallographic poses within the commonly accepted 2.0 & Aring; threshold (RMSD = 1.42 & Aring; for 4DVR and 1.18 & Aring; for 4INB), supporting the internal reliability of the docking setup. The compound displayed favorable predicted binding to gp120 (MolDock -115.434 kcal/mol; rerank -96.752 kcal/mol) and capsid (MolDock -117.299 kcal/mol; rerank -92.212 kcal/mol) relative to the targetspecific co-crystallized ligand benchmarks, without implying confirmed antiviral activity. Pharmacophore mapping supported the conservation of hydrophobic/aromatic features within both binding cavities. All-atom molecular dynamics simulations (100 ns) indicated stable computational complexes, with lower ligand positional drift in gp120 (approximately 0.90 f 0.12 & Aring;) than in capsid (approximately 2.4 f 0.33 & Aring;). MM/GBSA calculations qualitatively favored gp120 binding (Delta Gbind approximately -36.34 f 24.06 kcal/mol) over capsid (approximately -22.42 f 28.49 kcal/mol), although the large deviations indicate that these values should be interpreted as comparative computational trends. Collectively, the crystallographic and computational data support 2-benzothiazolyl sulfide as a scaffold requiring direct antiviral and target-binding validation rather than as an experimentally established anti-HIV agent.

To identify potential allosteric modulators targeting the allosteric site of the CDC34-UBC protein-protein interaction (PPI) complex, the current study employs advanced in-silico methods, including similarity searches, molecular docking, pharmacokinetics, and molecular dynamics (MD) simulation. A similarity search of 26,318 allosteric kinase inhibitors from ChemDiv was performed with the seven known standard molecules. Highly similar molecules were docked into the allosteric site of CDC34-UBC, and the resulting higher-affinity molecules were assessed for pharmacokinetics and absolute binding affinity. By following the above workflow, a total of four drug-like chemical entities, namely E612-1064, G681-0837, C076-0187, and K284-1783, were identified as potential hits for CDC34-UBC. The comparative analysis with one of the standard molecules (ASD06112004) revealed either better or comparable binding affinity towards CDC34-UBC. The binding energies from PLANTS, AutoDock Vina, and KDeep were found to be -112.15, -10.10, and − 8.70 kcal/mol; -110.35, -10.20, and − 9.61 kcal/mol; -95.69, -10.20, and − 10.31 kcal/mol; and − 96.11, -10.00, and − 10.10 kcal/mol for E612-1064, G681-0837, C076-0187, and K284-1483, respectively. Several parameters indicated that the protein backbone did not deviate by more than 2.18 Å from its initial position in any frame of the 100 ns MD simulations, indicating stability with the proposed molecules. The MM-GBSA energies for E612-1064, G681-0837, C076-0187, and K284-1483 were − 17.80, -34.27, -18.80, and − 9.04 kcal/mol, respectively, indicating that the molecules were potential in nature. Hence, selected molecules may act as allosteric kinase inhibitors targeting the CDC34-UBC complex, paving the way for new therapies for diseases linked to disruptions of the ubiquitin-proteasome system.

BACKGROUND:Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria Mycobacterium tuberculosis (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant role in activating the long-chain fatty acids. RESEARCH DESIGN & METHODS:This study aims to generate novel compounds using Machine Learning (ML) algorithms to inhibit this synthase. Experimentally derived bioactive compounds were chosen from ChEMBL and used as inputs for effective molecule generation by Reinvent4. The library of new molecules generated was subjected to a two-tiered molecular docking protocol, and the results were further studied to obtain a binding free energy check. RESULTS:The ML-based de novo drug design (DNDD) approach successfully generated a diverse library of novel molecules targeting Fatty Acyl-CoA synthase. After rigorous molecular docking and binding free energy analysis, four new compounds were identified as potential lead candidates with promising inhibitory effects on Mtb lipid metabolism. CONCLUSIONS:The study demonstrated the effectiveness of a machine-learning approach in generating novel drug candidates against Mtb. The identified hit compounds show potential as inhibitors of Fatty Acyl-CoA synthase, offering a new avenue for developing treatments for tuberculosis, particularly in combating drug-resistant strains.

Phenolic plant metabolites, including hydroxytyrosol, tyrosol, homovanillic alcohol, and their acetate derivatives, have emerged as potent antioxidants and promising therapeutic candidates for neurodegenerative disorders. These compounds exhibit dual functionality by efficiently scavenging reactive free radicals and targeting key protein residues, thereby alleviating oxidative stress and preventing cellular damage. Using multiscale in silico methodologies, their interactions with peroxyl (ROO•) and hydroperoxyl (HOO•) radicals, as well as with Monoamine Oxidase A (MAO-A), a pivotal enzyme in Parkinson's disease, were systematically investigated. Density Functional Theory (DFT) analyses illustrate radical stabilization pathways, supported by MEP, SD, NBO, FMO, and Fukui function descriptors. Hirshfeld surface analysis (HSA) and QTAIM further reveal strong binding hotspots, predominantly stabilized by conventional hydrogen bonding complemented with hydrophobic non-covalent contacts. ADMET profiling underscored favorable pharmacokinetic properties and drug-likeness. Finally, molecular docking and molecular dynamics (MD) simulations confirmed their stable accommodation within the MAO-A catalytic pocket, highlighting significant binding affinities and critical interacting residues. Overall, these findings establish hydroxytyrosol, tyrosol and homovanillic alcohol derivatives as potential multifunctional neuroprotective agents against Parkinson's disease.

The Carbohydrate Recognition Domain (CRD) of immune system’s c-type lectin receptors (CLRs) preferentially interacts with the Capsular Polysaccharides (CPS) units. Implicit Ca2+ ions are crucial to CRD function. Increment of the ionic concentration explicitly affects the CPS recognition by CRD many-fold. DC-SIGN is one such CLR that acts for the differential recognition of the microbial CPS. The CPS mannotriose had the lowest binding energy (ΔG -4.7 kcal/mol) and the maximum affinity for DC-SIGN with implicit Ca2+ ion. In the present investigation the ligand affinity increases with the rise of Ca2+ concentration up to 1.5 M. Again, within the CRD the residues viz; Glutamate (347), Proline (348), and Asparagine (349) (EPN) were reported previously as essential for CPS unit coordination. Our analysis demonstrated that besides the EPN residues, CPS unit interacts with the neighboring Asparagine (350), Glutamate (354) and Asparagine (355) residues. Thus, these residues were replaced one at a time with Alanine (a charge neutral residue) to test their effect on the contact event. The CRD loses its affinity for recognition on the N350A, E354A, and D355A substitutions. Thus, this heterogeneity of CRD recognition towards Carbohydrate provides fresh information about the immune system’s theragnostic function. This new understanding of Ca2+-induced recognition may help design new theragnostic applications that boost our immune defenses against pathogenic evasion.

INTRODUCTION:SYK (Spleen Tyrosine Kinase) regulates immune response and is a promising target for cancer, sepsis, and allergy therapies. This study aims to create novel compounds that serve as alternative inhibitors for cancer treatments targeting SYK. METHODS:A thorough combination of machine learning (ML) and physics-based methods was employed to achieve these goals, encompassing de novo design, multitier molecular docking, absolute binding affinity computation, and molecular dynamics (MD) simulation. RESULTS:A total of 5576 novel molecules with key pharmacophoric features were generated using an ML-driven de novo approach against 21 diaminopyrimidine carboxamide analogs. Pharmacokinetic and toxicity evaluation assisted by the ML approach revealed that 4353 chemical entities fulfilled the acceptable pharmacokinetic and toxicity profiles. By screening through binding energy threshold from the physics-based multitier molecular docking, and ML-assisted absolute binding affinity identified the top four molecules such as RI809 (2-([1,1'-biphenyl]-3-ylmethyl)-4-((2- aminocyclohexyl)oxy)benzamide), RI1393 (4-((2-aminocyclohexyl)amino)-2-(3-(1-methyl-1Hpyrazol- 5-yl)-4-(trifluoromethyl)benzyl)benzamide), RI2765 (2-([1,1'-biphenyl]-3-ylmethyl)-4-((4- aminocyclohexyl)methyl)benzamide), and RI3543 (2-([1,1'-biphenyl]-2-ylmethyl)-4-(piperidin-3- yloxy)benzamide). The final molecules identified exhibit a strong affinity for SYK, attributed to their structural diversity and notable pharmacophoric characteristics. All-atom MD simulations showed that each final molecule retained significant binding interactions with SYK and stability in dynamic states, indicating their potential as anticancer agents. Calculated binding free energy for selected molecules using molecular mechanics with generalized Born and surface area (MMGBSA) ranged from -6 to -35 kcal/mol, indicating strong SYK affinity. CONCLUSION:In conclusion, the integration of AI and physics-based methods successfully developed promising SYK inhibitors with significant potential. The molecules reported could be vital anticancer agents subjected to experimental validation.

Coumarin derivatives have been explored as highly promising antioxidants due to their significant binding mechanisms with radicals and protein residues. Herein, we have selected a few bioactive coumarin derivatives to investigate their effectiveness in complexation with alkoxy (RO') and hydroperoxyl (HOO') radicals as well as with carbonic anhydrase VII (CA VII) protein. To gain further insights, in silico studies viz., DFT, pharmacokinetic evaluation (ADMET) and drug-likeness assessments, etc., are useful to analyze the complex formation mechanism with radicals and protein residues. Again, Molecular Electrostatic Potential (MEP), Spin Density (SD) and Natural Bond Orbital (NBO) studies display superior binding interactions with the protein residues characterized by conventional H-bonding along with other hydrophobic interactions. Moreover, molecular docking, dynamics and physicochemical analysis reveal that all coumarin derivatives interact effectively within the internal cavity of CA VII, detailing binding affinities and specific protein residues involved.

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA). Initially, the entire database of ∼ 119000 molecules was sorted out through drug-likeness. Further, the molecular docking study was conducted to reduce the chemical space. The standard TB drug molecule's binding energy was considered a threshold, and molecules found with lower affinity were removed for further analyses. Finally, the molecules were checked for the pharmacokinetic and toxicity studies, and compounds found to have acceptable pharmacokinetic parameters and were non-toxic were considered as final promising molecules for InhA. The above approach further evaluated five molecules for ML-based toxicity and synthetic accessibility assessment. Not a single molecule was found toxic and each of them was revealed as easy to synthesise. The complex between InhA and proposed and standard molecules was considered for molecular dynamics simulation. Several statistical parameters showed the stability between InhA and the proposed molecule. The high binding affinity was also found for each of the molecules towards InhA using the MM-GBSA approach. Hence, the above approaches and findings exposed the potentiality of the proposed molecules against InhA.

Peregrin is marked as a potential drug target due to its pivotal role in the epigenetic maintenance of cellular metabolism. It is counted among those of the bromodomain-containing protein family. The protein binds to the acetylated lysine of N-terminus histone proteins of the nucleosomes. This bound form neutralizes the otherwise positively charged histone and, thus, crucially impacts the DNA replication mechanism. In several diseases like cancer, bone loss, and leukemia, this peregrin binding impacts the genes' overexpression. Hence, the present study, computational screening of the bromodomain-specific inhibitors from a library of 5430 compounds retrieved from the ChemDiv database. Two different scoring functions based molecular docking studies have been employed for molecular interaction analysis. Followed by an artificial intelligence-based absolute binding affinity prediction has been confirmed through K Deep , which follows a machine learning step to obtain a precise binding energy score for the set of best compounds in the screening process. The resultant compounds were then screened for pharmacokinetics and drug-likeliness properties. Using molecular dynamics (MD) simulation analysis, the dynamic behavior of the four suggested compounds and Peregrin has been investigated for a 100 ns period. Identified compounds are found to be sufficiently effective in holding their strong interaction affinity toward Peregrin for an adequate time span, and structural integrity is also found to be comparable with the standard compound considered in the study. Mostly, all four potent compounds (K788-9421, S357-0084, S3570893, S357-0915) are hydrophobic and evaluated for thermodynamics property analysis using MM-GBSA and free energy landscape (FEL) calculations. However, among all, K788-9421 shows comparatively better and energetically most favorable interactions with peregrin. Overall, study findings can lead to further research in developing next-generation broad-spectrum Peregrin inhibitors-modulators; they need extensive experimental validation for considering the compounds for clinical-trial application for being an excellent drug-candidate entity against Peregrin protein.

Antimicrobial peptides have gradually gained advantages over small molecule inhibitors for their multifunctional effects, synthesising accessibility and target specificity. The current study aims to determine an antimicrobial peptide to inhibit PknB, a serine/threonine protein kinase (STPK), by binding efficiently at the helically oriented hinge region. A library of 5626 antimicrobial peptides from publicly available repositories has been prepared and categorised based on the length. Molecular docking using ADCP helped to find the multiple conformations of the subjected peptides. For each peptide served as input the tool outputs 100 poses of the subjected peptide. To maintain an efficient binding for relatively a longer duration, only those peptides were chosen which were seen to bind constantly to the active site of the receptor protein over all the poses observed. Each peptide had different number of constituent amino acid residues; the peptides were classified based on the length into five groups. In each group the peptide length incremented upto four residues from the initial length form. Five peptides were selected for Molecular Dynamic simulation in Gromacs based on higher binding affinity. Post-dynamic analysis and the frame comparison inferred that neither the shorter nor the longer peptide but an intermediate length of 15 mer peptide bound well to the receptor. Residual substitution to the selected peptides was performed to enhance the targeted interaction. The new complexes considered were further analysed using the Elastic Network Model (ENM) for the functional site’s intrinsic dynamic movement to estimate the new peptide’s role. The study sheds light on prospects that besides the length of peptides, the combination of constituent residues equally plays a pivotal role in peptide-based inhibitor generation. The study envisages the challenges of fine-tuned peptide recovery and the scope of Machine Learning (ML) and Deep Learning (DL) algorithm development. As the study was primarily meant for generation of therapeutics for Tuberculosis (TB), the peptide proposed by this study demands meticulous invitro analysis prior to clinical applications.

Tumor necrosis factor alpha (TNF-α) is the major cause of inflammation in autoimmune diseases like rheumatoid arthritis (RA). It's mechanisms of signal transduction through nuclear factor kappa B (NF-kB) pathway via small molecules such as metabolite crosstalk are still elusive. In this study, we have targeted TNF-α and NF-kB through metabolites of RA, to inhibit TNF-α activity and deter NF-kB signaling pathways, thereby mitigating the disease severity of RA. TNF-α and NF-kB structure was obtained from PDB database and metabolites of RA were selected from literature survey. In-silico studies were carried out by molecular docking using AutoDock Vina software and further, known TNF-α and NF-kB inhibitors were compared and revealed metabolite's capacity to targets the respective proteins. Most suitable metabolite was then validated by MD simulation to verify its efficiency against TNF-α. Total 56 known differential metabolites of RA were docked with TNF-α and NF-kB compared to their corresponding inhibitor compounds. Four metabolites such as Chenodeoxycholic acid, 2-Hydroxyestrone, 2-Hydroxyestradiol (2-OHE2), and 16-Hydroxyestradiol were identified as a common TNF-α inhibitor's having binding energies ranging from -8.3 to -8.6 kcal/mol, followed by docking with NF-kB. Further, 2-OHE2 was selected because of having binding energy -8.5 kcal/mol, found to inhibit inflammation and the effectiveness was validated by root mean square fluctuation, radius of gyration and molecular mechanics with generalized born and surface area solvation against TNF-α. Thus 2-OHE2, an estrogen metabolite was identified as the potential inhibitor, attenuated inflammatory activation and can be utilized as a therapeutic target to disseminate severity of RA.

Microalgae can accumulate a high amount of lipids under stress conditions, and, for this reason, it is of great interest in biodiesel production. To explore another promising candidate, growth, pigments, and biochemical composition of Lobochlamys sp. GUEco1006 on various growth media viz. Bold’s Basal Medium (BBM), Bristol, BG11 (Blue-green 11 medium), and Chu’s 10 were studied initially. The alga grown on BBM showed the best growth with maximum carotenoid content (4.8 µg⁄ml), lipid content (22.27%), and lipid productivity (61.5 mg L− 1d− 1). Further, the effect of salinity on pigments, lipid accumulation, and productivity of the test organism in the BBM medium was explored. The microalga attained significantly higher carotenoid content (11.13 µg⁄ml), lipid content (81.2% per dry cell weight), and lipid productivity (188.8 mg L− 1d− 1) in 0.3 M NaCl treated culture over control. This study demonstrated that salinity could act as a lipid trigger for Lobochlamys sp. GUEco1006, and it can be a promising candidate for biodiesel production in the near future.

Due to the current Coronavirus (COVID-19) pandemic, the rapid discovery of a safe and effective vaccine is an essential issue. Consequently, this study aims to predict a potential COVID-19 peptide-based vaccine utilizing the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics approach. To achieve this goal, several Immune Epitope Database (IEDB) tools, molecular docking, and safety prediction servers were used. According to the results, The Spike peptide SQCVNLTTRTQLPPAYTNSFTRGVY is predicted to have the highest binding affinity to the B-Cells. The Spike peptide FTISVTTEI has the highest binding affinity to the Major Histocompatibility Complex class 1 (MHC I) Human Leukocyte Allele HLA-B*1503 (according to the MDockPeP and HPEPDOCK servers, docking scores were −153.9 and −229.356, respectively). The Nucleocapsid peptides KTFPPTEPK and RWYFYYLGTGPEAGL have the highest binding affinity to the MHC I HLA-A0202 allele and the three the Major Histocompatibility Complex class 2 (MHC II) Human Leukocyte Allele HLA-DPA1*01:03/DPB1*02:01, HLA-DQA1*01:02/DQB1-*06:02, HLA-DRB1, respectively. Docking scores of peptide KTFPPTEPK were −153.9 and −220.876. In contrast, docking scores of peptide RWYFYYLGTGPEAGL were ranged from 218 to 318. Furthermore, those peptides were predicted as non-toxic and non-allergen. Therefore, the combination of those peptides is predicted to stimulate better immunological responses with respectable safety.

Glial Fibrillary Acidic Protein (GFAP) is an intermediate-filament (IF) protein that maintains the astrocytes of the Central Nervous System in Human. This is differentially expressed during serological studies in inflamed condition such as Rheumatoid Arthritis (RA). Therefore, it is of interest to glean molecular insight using a model of GFAP (49.88 kDa) due to its crystallographic nonavailability. The present study has been taken into consideration to construct computational protein model using Modeller 9.11. The structural relevance of the protein was verified using Gromacs 4.5 followed by validation through PROCHECK, Verify 3D, WHAT-IF, ERRAT and PROVE for reliability. The constructed three dimensional (3D) model of GFAP protein had been scrutinized to reveal the associated functions by identifying ligand binding sites and active sites. Molecular level interaction study revealed five possible surface cavities as active sites. The model finds application in further computational analysis towards drug discovery in order to minimize the effect of inflammation.

Asthma is a multi factorial disease characterized by airflow obstruction, wheezing and dysonea and for this many genes are responsible. In this study, an attempt has been made to identify genes along with the proteins encoded by them which are responsible for Asthma along with clinical trials. In the practice of Ayurveda, it's well established that shirish (Albizzia lebbeck) can be used as a treatment of Asthma. However, how the compounds of shirish bark functions at the molecular level is yet unknown. The present study shows an insilico molecular interaction studies and clinical trials as well for the compounds of shirish bark. The results show how the compounds of shirish bark affect the target proteins of asthma which actually will aid in the design of putative inhibitor. Among the screened 9 (nine) compounds of shirish, all the compounds are giving good binding affinity to the selected proteins and among them lebbecacidin interacts potentially and is also showing lowest binding energy with IL-5, one of the target proteins. Lebbecacidin has satisfied all the criteria for a potential lead with drug likeliness 1.26, drug score 0.83, high solubility and high cell membrane permeability and it has also been calculated to be non toxic which was also validated by clinical studies.