Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

Bacterial cytochrome P450s: a bioinformatics odyssey of substrate discovery

Bacterial P450 cytochromes (BacCYPs) are versatile heme-containing proteins responsible for oxidation reactions on a wide range of substrates, contributing to the production of valuable natural products with limitless biotechnological potential. While the sequencing of microbial genomes has provided a wealth of BacCYP sequences, functional characterization lags behind, hindering our understanding of their roles. This study employs a comprehensive approach to predict BacCYP substrate specificity, bridging the gap between sequence and function. We employed an integrated approach combining sequence and functional data analysis, genomic context exploration, 3D structural modeling with molecular docking, and phylogenetic clustering. The research begins with an in-depth analysis of BacCYP sequence diversity and structural characteristics, revealing conserved motifs and recurrent residues in the active site. Phylogenetic analysis identifies distinct groups within the BacCYP family based on sequence similarity. However, our study reveals that sequence alone does not consistently predict substrate specificity, necessitating additional perspectives. The study delves into the genetic context of BacCYPs, utilizing neighboring gene information to infer potential substrates, a method proven very effective in many cases. Molecular docking is employed to assess BacCYP-substrate interactions, confirming potential substrates and providing insights into selectivity. Finally, a comprehensive strategy is proposed for predicting BacCYP substrates, involving all the evaluated approaches. The effectiveness of this strategy is demonstrated with two case studies, highlighting its potential for substrate discovery.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Identification of 1, 2, 4-Triazine and Its Derivatives Against Lanosterol 14-Demethylase (CYP51) Property of Candida albicans: Influence on the Development of New Antifungal Therapeutic Strategies

This research aims to find out whether the 1, 2, 4-triazine and its derivatives have antifungal effects and can protect humans from infection with Candida albicans. Molecular docking and molecular dynamic simulation are widely used in modern drug design to target a particular protein with a ligand. We are interested in using molecular docking and molecular dynamics modeling to investigate the interaction between the derivatives of 1, 2, 4-triazine with enzyme Lanosterol 14-demethylase (CYP51) of Candida albicans. The inhibition of Candida albicans CYP51 is the main goal of our research. The 1, 2, 4-triazine and its derivatives have been docked to the CYP51 enzyme, which is involved in Candida albicans Multidrug Drug Resistance (MDR). Autodock tools were used to identify the binding affinities of molecules against the target proteins. Compared to conventional fluconazole, the molecular docking results indicated that each drug has a high binding affinity for CYP51 proteins and forms unbound interactions and hydrogen bonds with their active residues and surrounding allosteric residues. The docking contacts were made using a 10 ns MD simulation with nine molecules. RMSD, RMSF, hydrogen bonds, and the Rg all confirm these conclusions. In addition, these compounds were expected to have a favorable pharmacological profile and low toxicity. The compounds are being offered as scaffolds for the development of new antifungal drugs and as candidates for future in vitro testing.

Identification of a Chlorogenic Ester as a Monoamine Oxidase (MAO-B) Inhibitor by Integrating "Traditional and Machine Learning" Virtual Screening and In Vitro as well as In Vivo Validation: A Lead against Neurodegenerative Disorders?

Parkinson's disease (PD) is the furthermost motor disorder of adult-onset dementia connected to memory and other cognitive abilities. Monoamine oxidases (MAOs) have gained significant attention in recent years owing to their possible therapeutic use against PD. Expression of MAO-B has been found to be elevated in PD patients for increased uptake of dopamine, producing hydrogen peroxide and finally causing neuronal injury. In this work, two new compounds have been identified as leads against MAO-B, and one of those compounds has been validated in vitro and in vivo. From the Protein Data Bank, MAO-B protein structures complexed with selegiline, 6-hydroxy-N-propargyl-1(R)-aminoindan, or a chromen derivative have been selected as templates for shape-based virtual screening (SB-VS) against the Traditional Chinese Medicinal (TCM) natural database. In parallel, using machine learning, a molecular-descriptor-based support vector model (SVM) was prepared and screened. For this purpose, naïve Bayesian, logistic regression, and random forest strategies were employed with the best specific molecular descriptor, which yielded a model with an overall accuracy (Q) of 0.81. Two common hit compounds lead-1 and lead-2 resulting from both shape and SVM screenings were analyzed through molecular docking and molecular dynamics (MD) simulation (200 ns). Also, from trajectory analysis such as molecular mechanics generalized Born surface area (MMGB/SA) and the residual interaction network (RIN) analyzer, both leads were found to bind at the active site with a favorable correlated motion, including domain movements. Lead-2, which is a chlorogenic ester, was synthesized and found to have no cytotoxic effect up to 50 μg/mL on Neuro-2A cells. The significant reactive oxygen species (ROS) scavenging activity by lead-2 could be correlated to its neuroprotective efficacy. Its capacity to inhibit human MAO-B through a competitive mode could be observed. An experimental zebra fish model confirms the neuroprotection by lead-2 by assessing the locomotor activities under malathion influence and treatment of lead-2. Also, histopathology analysis revealed that lead-2 could slow down degeneration in the brain. The present study emphasizes that integrating machine learning in parallel with traditional virtual screening may be useful to identify effective lead compounds for a given target.

Interpretations on the Interaction between Protein Tyrosine Phosphatase and E7 Oncoproteins of High and Low-Risk HPV: A Computational Perception

The most prevalent and common sexually transmitted infection is caused by human papillomavirus (HPV) among sexually active women. Numerous genotypes of HPV are available, among which the major oncoproteins E6 and E7 lead to the progression of cervical cancer. The E7 oncoprotein interacts with cytoplasmic tumor suppressor protein PTPN14, which is the key regulator of cellular growth control pathways effecting the reduction of steady-state level. Disrupting the interaction between the tumor suppressor and the oncoprotein is vital to cease the development of cancer. Hence, the mechanism of interaction between E7 and tumor suppressor is explored through protein-protein and protein-ligand binding along with the conformational stability studies. The obtained results state that the LXCXE domain of HPV E7 of high and low risks binds with the tumor suppressor protein. Also, the small molecules bind in the interface of E7-PTPN14 that disrupts the interaction between the tumor suppressor and oncoprotein. These results were further supported by the dynamics simulation stating the stability over the bounded complex and the energy maintained during postdocking as well as postdynamics calculations. These observations possess an avenue in the drug discovery that leads to further validation and also proposes a potent drug candidate to treat cervical cancer caused by HPV.

In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling, molecular docking, and dynamics simulations

The rapid increase of HIV-1 infection throughout the globe has a high demand for a superior drug with lesser side effects. LEDGF/p75, the human Lens Epithelium-Derived Growth Factor is identified as a promising cellular cofactor with integrase in facilitating the viral replication in an early stage by acting as a tethering factor in the pre-integration to the chromatin. Therefore, the present study was designed to identify a potent inhibitor by applying an E-pharmacophore based virtual screening, molecular docking, and dynamics simulation approaches. Finally, ZINC22077550 and ZINC32124441 were best identified potent molecules with the efficient binding affinity, strong hydrogen bonding, and acceptable pharmacological properties to hamper the interaction between integrase and LEDGF/p75. Further, the DFT and MDS studies were also analyzed, and shown a favorable energetic state and dynamic stability then reference compound. In conclusion, we suggest that these findings could be novel therapeutics in the future and may increase the lifespan of individuals suffering from viral infection.

Pharmacophore based virtual screening, molecular docking and molecular dynamic simulation studies for finding ROS1 kinase inhibitors as potential drug molecules

Proto-oncogene receptor tyrosine kinase ROS-1 is one of the clinically important biomarker and plays a crucial role in regulation of a number of cellular functions including cell proliferation, migration and angiogenesis. Recently, inhibition of ROS1 kinase has proven to be a promising target of anticancer drugs for non-small cell lung cancer (NSCLC). The very few compounds have been used as potent drug molecules so far and the selective ROS1 inhibitors are relatively rare. Besides the currently available drugs such as Crizotinib and PF-06463922 are becoming sensitive due to mutations in the ROS1 protein. To curtail the problem of the resistant, present study was designed to identify the potent inhibitors against ROS1. Three different screening approaches such as structure based, Atom-based and pharmacophore based screening were carried out against commercially available databases and the retrieved best hits were further evaluated by Lipinski's filter. Thereafter the lead molecule was subjected to pocket specific docking with ROS1. The results show that, total of 9 molecules (3 from each screening) has good docking score (with range of -9.288 to -12.49 Kcal/Mol) and binding interactions within the active site of ROS1. In order to analyze the stability of the ligand- protein complexes, molecular dynamics simulation was performed. Thus, these identified potential lead molecules with good binding score and binding affinity with ROS1 may act as the potent ROS1 inhibitor, and that are worth considering for further experimental studies.Communicated by Ramaswamy H. Sarma.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

BACKGROUND

The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

OBJECTIVE

This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

METHODS

31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

RESULTS

Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

CONCLUSION

Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Computational analysis identifies druggable mutations in human rBAT mediated Cystinuria

Culex quinquefasciatus Cqm1 protein acts as the receptor for Lysinibacillus sphaericus mosquito-larvicidal binary (BinAB) toxin that is used worldwide for mosquito control. We found amino acid transporter protein, rBAT, as phylogenetically closest Cqm1 homolog in humans. The present study reveals large evolutionary distance between Cqm1 and rBAT, and rBAT ectodomain lacks the sequence motif which serves as binding-site for the BinAB toxin. Thus, BinAB toxin can be expected to remain safe for humans. rBAT (heavy subunit; SLC3A1) and catalytic b^0,+AT (light subunit; SLC7A9), linked by single disulfide bond, mediate renal reabsorption of cystine and dibasic amino acids in Na⁺ independent manner. Mutations in rBAT cause type I Cystinuria disease which shows global prevalence, and rBAT can be thought as an important pharmacological target. However, 3D structures of rBAT and b^0,+AT, the two components of b^0,+ heteromeric amino acid transporter systems, are not available. We constructed a reliable homology model of rBAT using Cqm1 coordinates and that of transmembrane b^0,+AT subunit using LAT1 coordinates. Mapping of pathogenic mutations onto rBAT ectodomain revealed their scattered distribution throughout the rBAT protein. Further, our computational simulations-based scoring of several known deleterious mutations of rBAT revealed that mutations those do not compromise the protein fold and stability, are localized on the same face of the molecule. These residues are expected to interact with the b^0,+AT transporter. The present study thus identifies druggable sites on rBAT that could be targeted for the treatment of type I Cystinuria.Communicated by Ramaswamy H. Sarma.

Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7

High risk human papillomaviruses are highly associated with the cervical carcinoma and the other genital tumors. Development of cervical cancer passes through the multistep process initiated from benign cyst to increasingly severe premalignant dysplastic lesions in an epithelium. Replication of this virus occurs in the fatal differentiating epithelium and involves in the activation of cellular DNA replication proteins. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers constrains the cells into S-phase constructing an environment favorable for genome replication and cell proliferation. To date, no suitable drug molecules exist to treat HPV infection whereas anticipation of novel anti-HPV chemotherapies with distinctive mode of actions and identification of potential drugs are crucial to a greater extent. Hence, our present study focused on identification of compounds analogue to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems towards treatment of cancer. A three dimensional similarity search on the small molecule library from natural product database using EGCG identified 11 potential small molecules based on their structural similarity. The docking strategies were implemented with acquired small molecules and identification of the key interactions between protein and compounds were carried out through binding free energy calculations. The conformational changes between the apoprotein and complexes were analyzed through simulation performed thrice demonstrating the dynamical and structural effects of the protein induced by the compounds signifying the domination. The analysis of the conformational stability provoked us to describe the features of the best identified small molecules through electronic structure calculations. Overall, our study provides the basis for structural insights of the identified potential identified small molecules and EGCG. Hence, the identified analogue of EGCG can be potent inhibitors against the HPV 16 E7 oncoprotein.

Identification of Potential Dual Negative Allosteric Modulators of Group I mGluR Family: A Shape Based Screening, ADME Prediction, Induced Fit Docking and Molecular Dynamics Approach Against Neurodegenerative Diseases

BACKGROUND

Glutamate is the principal neurotransmitter in the human brain that exerts its effects through ionotropic glutamate receptors (iGluRs) and metabotropic glutamate receptors (mGluRs). The mGluRs are a class of C GPCRs that play a vital role in various neurobiological functions, mGluR1 and mGluR5 are the two receptors distributed throughout the brain involved in cognition, learning, memory, and other important neurological processes. Dysfunction of these receptors can cause neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, X-fragile syndrome, anxiety, depression, etc., hence these receptors are high profile targets for pharmaceutical industries.

OBJECTIVE

The objective of our study is to find the novel dual negative allosteric modulators to regulate both mGluR1 and mGluR5.

METHODS

In this study, shape screening protocol was used to find the dual negative allosteric modulators for both mGluR1 and mGluR5 followed by ADME prediction, induced-fit docking (IFD) and molecular dynamics simulations. Further, DFT analysis and MESP studies were carried out for the selected compounds.

RESULTS

Around 247 compounds were obtained from the eMolecules database and clustered through the CANVAS module and filtered with ADME properties. Furthermore, IFD revealed that the top four compounds (16059796, 25004252, 4667236 and 11670690) having good protein-ligand interactions and binding free energies. The molecular electrostatic potential of the top compounds shows interactions in the amine group and the oxygen atom in the negative potential regions. Finally, molecular dynamics simulations were performed with all the selected as well as the reported compound 29 indicates that the screened hits have better stability of protein ligand complex.

CONCLUSION

Hence, from the results, it is evident that top hits 16059796, 25004252, 4667236 and 11670690 could be a novel and potent dual negative allosteric modulators for mGluR1 and mGluR5.

Discovery of human autophagy initiation kinase ULK1 inhibitors by multi-directional in silico screening strategies

Autophagy is a self-catabolic mechanism employed by cancer cells to acquire nutrients and energy in times of stress conditions, thereby leading to its progression and survival. Thus, autophagy inhibition has emerged as a new paradigm in the area of cancer treatment. Here, we leverage multi-dimensional screening campaigns aim to identify potent inhibitors against an early and an essential autophagic kinase, ULK1 from DrugBank database. In particular, receptor-based hypothesis, pharmacophore hypothesis, e-pharmacophore hypothesis and shape similarity-based screening algorithm were employed. Of note, the results of the different algorithm were then integrated to eliminate the false positive prediction. Moreover, the inhibitory activities and PK/PD parameters of the leads were tested by Glide and Qikprop algorithm. This resulted in a set of four hits namely; DB12686, DB08341, DB07936, and DB07163. Finally, molecular dynamics simulation was performed using the GROMACS package, to validate the binding kinetics of the hit compound. The compound activity in vitro was assessed by PASS algorithm, highlights the anti-cancer activities of the hits. The structural insights reveal existence of functional moieties such as piperidine carboxamide, benzenesulfonamide, benzamide, and isoindolone in the resultant hits which plays a major role in the anti-cancer activity. Overall, we strongly believe that these ULK1 antagonists could be novel and potent drug candidates for future cancer therapeutics.

Identification of small molecule enzyme inhibitors as broad-spectrum anthelmintics

Targeting chokepoint enzymes in metabolic pathways has led to new drugs for cancers, autoimmune disorders and infectious diseases. This is also a cornerstone approach for discovery and development of anthelmintics against nematode and flatworm parasites. Here, we performed omics-driven knowledge-based identification of chokepoint enzymes as anthelmintic targets. We prioritized 10 of 186 phylogenetically conserved chokepoint enzymes and undertook a target class repurposing approach to test and identify new small molecules with broad spectrum anthelmintic activity. First, we identified and tested 94 commercially available compounds using an in vitro phenotypic assay, and discovered 11 hits that inhibited nematode motility. Based on these findings, we performed chemogenomic screening and tested 32 additional compounds, identifying 6 more active hits. Overall, 6 intestinal (single-species), 5 potential pan-intestinal (whipworm and hookworm) and 6 pan-Phylum Nematoda (intestinal and filarial species) small molecule inhibitors were identified, including multiple azoles, Tadalafil and Torin-1. The active hit compounds targeted three different target classes in humans, which are involved in various pathways, including carbohydrate, amino acid and nucleotide metabolism. Last, using representative inhibitors from each target class, we demonstrated in vivo efficacy characterized by negative effects on parasite fecundity in hamsters infected with hookworms.

Structure-based virtual screening toward the discovery of novel inhibitors for impeding the protein-protein interaction between HIV-1 integrase and human lens epithelium-derived growth factor (LEDGF/p75)

HIV-1 integrase is a unique promising component of the viral replication cycle, catalyzing the integration of reverse transcribed viral cDNA into the host cell genome. Generally, IN activity requires both viral as well as a cellular co-factor in the processing replication cycle. Among them, the human lens epithelium-derived growth factor (LEDGF/p75) represented as promising cellular co-factor which supports the viral replication by tethering IN to the chromatin. Due to its major importance in the early steps of HIV replication, the interaction between IN and LEDGF/p75 has become a pleasing target for anti-HIV drug discovery. The present study involves the finding of novel inhibitor based on the information of dimeric CCD of IN in complex with known inhibitor, which were carried out by applying a structure-based virtual screening concept with molecular docking. Additionally, Free binding energy, ADME properties, PAINS analysis, Density Functional Theory, and Enrichment Calculations were performed on selected compounds for getting a best lead molecule. On the basis of these analyses, the current study proposes top 3 compounds: Enamine-Z742267384, Maybridge-HTS02400, and Specs-AE-848/37125099 with acceptable pharmacological properties and enhanced binding affinity to inhibit the interaction between IN and LEDGF/p75. Furthermore, Simulation studies were carried out on these molecules to expose their dynamics behavior and stability. We expect that the findings obtained here could be future therapeutic agents and may provide an outline for the experimental studies to stimulate the innovative strategy for research community.

Synthesis, Molecular Docking Studies, and Antifungal Activity Evaluation of New Benzimidazole-Triazoles as Potential Lanosterol 14α-Demethylase Inhibitors

Due to anticandidal importance of azole compounds, a new series of benzimidazole-triazole derivatives(5a–5s)were designed and synthesized as ergosterol inhibitors. The chemical structures of the target compounds were characterized by spectroscopic methods. The final compounds were screened for antifungal activity againstCandida glabrata(ATCC 90030),Candida krusei(ATCC 6258),Candida parapsilosis(ATCC 22019), andCandida albicans(ATCC 24433). Compounds5iand5sexhibited significant inhibitory activity againstCandidastrains with MIC50values ranging from 0.78 to 1.56 μg/mL. Cytotoxicity results revealed that IC50values of compounds5iand5sagainst NIH/3T3 are significantly higher than their MIC50values. Effect of the compounds5iand5sagainst ergosterol biosynthesis was determined by LC-MS-MS analysis. Both compounds caused a significant decrease in the ergosterol level. The molecular docking studies were performed to investigate the interaction modes between the compounds and active site of lanosterol 14-α-demethylase (CYP51), which is as a target enzyme for anticandidal azoles. Theoretical ADME predictions were also calculated for final compounds.

Targeting CYP51 for drug design by the contributions of molecular modeling

CYP51 is an enzyme of sterol biosynthesis pathway present in animals, plants, protozoa and fungi. This enzyme is described as an important drug target that is still of interest. Therefore, in this work, we reviewed the structure and function of CYP51 and explored the molecular modeling approaches for the development of new antifungal and antiprotozoans that target this enzyme. Crystallographic structures of CYP51 of some organisms have already been described in the literature, which enable the construction of homology models of other organisms' enzymes and molecular docking studies of new ligands. The binding mode and interactions of some new series of azoles with antifungal or antiprotozoan activities has been studied and showed important residues of the active site. Molecular modeling is an important tool to be explored for the discovery and optimization of CYP51 inhibitors with better activities, pharmacokinetics, and toxicological profiles.

Interaction investigations of crustacean β-GBP recognition toward pathogenic microbial cell membrane and stimulate upon prophenoloxidase activation

In invertebrates, crustaceans' immune system consists of pattern recognition receptors (PRRs) instead of immunoglobulin's, which involves in the microbial recognition and initiates the protein-ligand interaction between hosts and pathogens. In the present study, PRRs namely β-1,3 glucan binding protein (β-GBP) from mangrove crab Episesarma tetragonum and its interactions with the pathogens such as bacterial and fungal outer membrane proteins (OMP) were investigated through microbial aggregation and computational interaction studies. Molecular recognition and microbial aggregation results of Episesarma tetragonum β-GBP showed the specific binding affinity toward the fungal β-1,3 glucan molecule when compared to other bacterial ligands. Because of this microbial recognition, prophenoloxidase activity was enhanced and triggers the innate immunity inside the host animal. Our findings disclose the role of β-GBP in molecular recognition, host-pathogen interaction through microbial aggregation, and docking analysis. In vitro results were concurred with the in silico docking, and molecular dynamics simulation analysis. This study would be helpful to understand the molecular mechanism of β-GBP and update the current knowledge on the PRRs of crustaceans.

Targeting the cyclin-binding groove site to inhibit the catalytic activity of CDK2/cyclin A complex using p27(KIP1)-derived peptidomimetic inhibitors

Functionally activated cyclin-dependent kinase 2 (CDK2)/cyclin A complex has been validated as an interesting therapeutic target to develop the efficient antineoplastic drug based on the cell cycle arrest. Cyclin A binds to CDK2 and activates the kinases as well as recruits the substrate and inhibitors using a hydrophobic cyclin-binding groove (CBG). Blocking the cyclin substrate recruitment on CBG is an alternative approach to override the specificity hurdle of the currently available ATP site targeting CDK2 inhibitors. Greater understanding of the interaction of CDK2/cyclin A complex with p27 (negative regulator) reveals that the Leu-Phe-Gly (LFG) motif region of p27 binds with the CBG site of cyclin A to arrest the malignant cell proliferation that induces apoptosis. In the present study, Replacement with Partial Ligand Alternatives through Computational Enrichment (REPLACE) drug design strategies have been applied to acquire LFG peptide-derived peptidomimetics library. The peptidomimetics function is equivalent with respect to substrate p27 protein fashion but does not act as an ATP antagonist. The combined approach of molecular docking, molecular dynamics (MD), and molecular electrostatic potential and ADME/T prediction were carried out to evaluate the peptidomimetics. Resultant interaction and electrostatic potential maps suggested that smaller substituent is desirable at the position of phenyl ring to interact with Trp217, Arg250, and Gln254 residues in the active site. The best docked poses were refined by the MD simulations which resulted in conformational changes. After equilibration, the structure of the peptidomimetic and receptor complex was stable. The results revealed that the various substrate protein-derived peptidomimetics could serve as perfect leads against CDK2 protein.