Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer.

Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer.

A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma.

BACKGROUND: Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32). AIM: The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL. METHODOLOGY: Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds. RESULT: MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies. CONCLUSION: Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Structural Insights into the Molecular Design of ROS1 Inhibitor for the Treatment of Non-Small Cell Lung Cancer (NSCLC).

BACKGROUND: Non-Small Cell Lung Cancer (NSCLC) alone is the leading cause of deaths worldwide. ROS1 is a receptor tyrosine kinase (RTK), eminently recognized as the stereotyped oncogenic driver. These RTKs trigger an array of physiological regulations via cellular signal transduction pathways, which are crucial for cancer development. This attributed ROS1 as an appealing and potential target towards the targeted cancer therapy. The present research aims to propound out an effective contemporary inhibitor for targeting ROS1 with a high affinity. METHODS: Molegro Virtual Docker (MVD) provided a flexible docking platform to find out the bestestablished drug as an inhibitor for targeting ROS1. A similarity search was accomplished against the PubChem database to acquire the corresponding inhibitor compounds regarding the Entrectinib (Pub- Chem ID: 25141092). These compounds were docked to procure the high-affinity inhibitor for the target protein via virtual screening. A comparative study between the control molecule (PubChem ID: 25141092)and the virtual screened compound(PubChem ID-25175866) was performed for the relative analysis of their salient features, which involved pharmacophore mapping, ADMET profiling, and BOILED-Egg plot. RESULTS: The virtual screened compound (PubChem ID-25175866) possesses the lowest rerank score (-126.623), and the comparative ADMET analysis also shows that it is a potential and effective inhibitor for ROS1 among the selected inhibitors. CONCLUSION: The present study provided a scope for the ROS1 inhibitor as significant prevention for nonsmall cell lung cancer (NSCLC). It can be upheld for future studies as a promising support via in vivo studies.

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.

Identification and Characterization of Lipopeptide Biosurfactant Producing Microbacterium sp Isolated from Brackish River Water.

BACKGROUND: Bioremediation has taken its call for removing pollutants for years. The oilcontaminated surroundings are majorly hazardous for sustaining life, but a great contribution to nature in the form of microorganisms. The complex carbon-hydrogen chain has served as classic raw material to chemical industries, which has perked up the hydrocarbon waste. Microbial remediation has been thus, focused to deal with the lacuna, where the new addition to this category is Microbacterium species. OBJECTIVES: The identification and characterization of lipopeptide biosurfactant producing Microbacterium spp. isolated from brackish river water. METHODS: The strain was isolated from an oil-contaminated lake. The strain was tested with all the other isolated species for oil degradation using screening protocols such as haemolysis, oil spread assay, BATH, E24, etc. The produced biosurfactant was extracted by acid precipitation, followed by solvent recovery. The strain with maximum potential was sequenced and was subjected to phylogeny assessment using in silico tools. RESULTS: Novel Microbacterium species produce the extracellular biosurfactant. The surface tension of Microbacterium was found to be 32mN/m, indicates its powerful surface tension-reducing property. The strain was optimized for the production of biosurfactant and the best results were obtained with sucrose (2%) and yeast extract (3%) medium at 7 pH and 40°C temperature. CONCLUSION: The isolate was confirmed to be a novel Microbacterium species that could produce 0.461 gm biosurfactant in 100 ml of the medium throughout a life cycle and novel strain of isolate was deposited to NCBI as Microbacterium spp. ANSKSLAB01 using an accession number: KU179507.

Computer-Aided Structure Prediction of Bluetongue Virus Coat Protein VP2 Assisted by Optimized Potential for Liquid Simulations (OPLS).

BACKGROUND: The capsid coated protein of Bluetongue virus (BTV) VP2 is responsible for BTV transmission by the Culicoides vector to vertebrate hosts. Besides, VP2 is responsible for BTV entry into permissive cells and hence plays a major role in disease progression. However, its mechanism of action is still unknown. OBJECTIVE: The present investigation aimed to predict the 3D structure of Viral Protein 2 of the bluetongue virus assisted by Optimized Potential for Liquid Simulations (OPLS), structure validation, and an active site prediction. METHODS: The 3D structure of the VP2 protein was built using a Python-based Computational algorithm. The templates were identified using Smith waterman's Local alignment. The VP2 protein structure validated using PROCHECK. Molecular Dynamics Simulation (MDS) studies were performed using an academic software Desmond, Schrodinger dynamics, for determining the stability of a model protein. The Ligand-Binding site was predicted by structure comparison using homology search and proteinprotein network analysis to reveal their stability and inhibition mechanism, followed by the active site identification. RESULTS: The secondary structure of the VP2 reveals that the protein contains 220 alpha helix atoms, 40 310 helix, 151 beta sheets, 134 coils and 424 turns, whereas the 3D structure of Viral Protein 2 of BTV has been found to have 15774 total atoms in the structure. However, 961 amino acids were found in the final model. The dynamical cross-correlation matrix (DCCM) analysis tool identifies putative protein domains and also confirms the stability of the predicted model and their dynamical behavior difference with the correlative fluctuations in motion. CONCLUSION: The biological interpretation of the Viral Protein 2 was carried out. DCCM maps were calculated, using a different coordinate reference frame, through which, protein domain boundaries and protein domain residue constituents were identified. The obtained model shows good reliability. Moreover, we anticipated that this research should play a promising role in the identification of novel candidates with the target protein to inhibit their functional significance.

In silico Insights on IL-6: A Potential Target for Multicentric Castleman Disease.

BACKGROUND: Multicentric Castleman Disease (MCD) is a confrontational lymphoproliferative disorder described by symptoms such as lymph node proliferation, unwarranted secretion of inflammatory cytokines, hyperactive immune system, and in severe cases, multiple organ dysfunction. Interleukin-6 (IL-6) is a pleiotropic cytokine which is involved in a large range of physiological processes in our body such as pro-inflammation, anti-inflammation, differentiation of T-cells and is reported to be a key pathological factor in MCD. In the case of MCD, it was observed that IL-6 is overproduced from T-cells and macrophages which disturb Hepcidin, a vital regulator of iron trafficking in macrophage. The present study endeavour to expound the inhibitor which binds to IL-6 protein receptor with high affinity. METHODS: MolegroVirtual Docker software was employed to find the best-established drug from the list of selected inhibitors of IL-6. This compound was subjected to virtual screening against PubChem database to get inhibitors with a very similar structure. These inhibitors were docked to obtain a compound binding with high affinity to the target protein. The established compound and the virtual screened compound were subjected to relative analysis of interactivity energy variables and ADMET profile studies. RESULTS: Among all the selected inhibitors, the virtual screened compound PubChem CID: 101119084 is seen to possess the highest affinity with the target protein. Comparative studies and ADMET analysis further implicate this compound as a better inhibitor of the IL-6 protein. CONCLUSION: Hence, this compound recognized in the study possesses high potential as an IL-6 inhibitor which might assist in the treatment of Multicentric Castleman Disease and should be examined for its efficiency by in vivo studies.

Identification of Potent VEGF Inhibitors for the Clinical Treatment of Glioblastoma, A Virtual Screening Approach.

Vascular endothelial growth factor (VEGF) expression could be found in all glioblastomas. VEGF takes part in numerous changes including the endothelial cell proliferation, the vasculature of solid tumor: its survival invasion, and migration, chemotaxis of bone marrow-derived progenitor cells, vasodilation and vascular permeability. VEGF inhibition can be a smart therapeutic strategy because it is extremely specific and less toxic than cytotoxic therapy. To establish better inhibition of VEGF than the current inhibitors, present study approach is by molecular docking, virtual screening to illustrate the inhibitor with superior affinity against VEGF to have a cautious pharma profile. To retrieve the best established and high-affinity high affinity molecule, Molegro Virtual Docker software was executed. The high-affinity scoring compounds were subjected to further similarity search to retrieve the drugs with similar properties from pubchem database. The completion of virtual screening reveals that PubChem compound SCHEMBL1250485 (PubChem CID: 66965667) has the highest affinity. The study of the drug-likeness was verified using OSIRIS Property Explorer software which supported the virtual screened result. Further ADMET study and drug comparative study strongly prove the superiority of the new established inhibitor with lesser rerank score and toxicity. Overall, the new inhibitor has higher potential to stop the expression of VEGF in glioblastoma and positively can be further analysed through In vitro studies.

Virtual Screening of IL-6 Inhibitors for Idiopathic Arthritis.

Juvenile idiopathic arthritis (JIA) is a heterogeneous disease characterized by the arthritis of unknown origin and IL6 is a known target for JIA. 20 known inhibitors towards IL-6 were screened and Methotrexate (MTX) having PubChem ID: 126941 showed high binding capacity with the receptor IL-6. The similarity searching with this compound gave 269 virtual screened compounds. The said screening presented 269 possible drugs having structural similarity to Methotrexate. The docking studies of the screened drugs separated the compound having PubChem CID: 122677576 (re-rank value of -140.262). Toxicity and interaction profile validated this compound for having a better affinity with the target protein. Conclusively, this study shows that according to ADMET profile and BOILED-Egg plot, the compound (PubChem CID: 122677576) obtained from Virtual Screen could be the best drug in future during the prevention of juvenile idiopathic arthritis. In the current study, the drug CID: 122677576 is a potent candidate for treating JIA. The pharmacophore study revealed that the drug CID: 122677576 is a non-inhibitor of CYP450 microsomal enzymes and was found to be non-toxic, similar to the established drug Methotrexate (CID: 126941). It has a lower LD50 value of 2.6698mol/kg as compared to the established compound having LD50 value as 23.4955mol/kg. Moreover, the compound was found to be non-carcinogenic.

Identification of High-Affinity Small Molecule Targeting IDH2 for the Clinical Treatment of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is symbolized by an increase in the number of myeloid cells in the bone marrow and an arrest in their maturation, frequently resulting in hematopoietic insufficiency (granulocytopenia, thrombocytopenia, or anemia) with or without leukocytosis either by a predominance of immature forms or a loss of normal hematopoiesis. IDH2 gene encodes for isocitrate dehydrogenase enzyme which is involved in the TCA cycle domino effect and converts isocitrate to alpha-ketoglutarate. In the U.S, the annual incidence of AML progressively increases with age to a peak of 12.6 per 100,000 adults of 65 years or older. Mutations in isocitrate dehydrogenase 2 (arginine 132) have been demonstrated to be recurrent gene alterations in acute myeloid leukemia (AML) by forming 2-Hydroxy alpha ketoglutarate which, instead of participating in TCA cycle, accumulates to form AML. The current study approaches by molecular docking and virtual screening to elucidate inhibitor with superior affinity against IDH2 and achieve a pharmacological profile. To obtain the best established drug Molegro Virtual Docker algorithm was executed. The compound AG-221 (Pub CID 71299339) having the high affinity score was subjected to similarity search to retrieve the drugs with similar properties. The virtual screened compound SCHEMBL16391748 (PubChem CID-117816179) shows high affinity for the protein. Comparative study and ADMET study for both the above compounds resulted in equivalent chemical properties. Virtual screened compound SCHEMBL16391748 (PubChem CID-117816179) shows the lowest re-rank score. These drugs are identified as high potential IDH2 inhibitors and can halt AML when validated through further In vitro screening.

A Computer - Aided Drug Designing for Pharmacological Inhibition of Mutant ALK for the Treatment of Non-small Cell Lung Cancer.

BACKGROUND: Lung cancer is the most common among all the types of cancer worldwide with 1.8 million people diagnosed every year, leading to 1.6 million deaths every year according to the American cancer society. The involvement of mutated Anaplasic Lymphoma Kinase (ALK) positive fusion protein in the progression of NSCLC has made a propitious target to inhibit and treat NSCLC. In the present study, the main motif is to screen the most effective inhibitor against ALK protein with the potential pharmacological profile. The ligands selected were docked with Molegro Virtual Docker (MVD) and CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with a permissible pharmacological profile. METHODS: The selected ligands were docked with Molegro Virtual Docker (MVD). With reference to the obtained compound with the lowest re-rank score, PubChem database was virtually screened to retrieve a large set of similar compounds which were docked to find the compound with higher affinity. Further comparative studies and in silico prediction included pharmacophore studies, proximity energy parameters, ADMET and BOILED-egg plot analysis. RESULTS: CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with preferable pharmacological profile and PubChem compound CID-123449015 came out as the most efficient virtually screened inhibitor. Interestingly, the contours of the virtual screened compound PubChem CID- 123449015 fall within our desired high volume cavity of protein having admirable property to control the ALK regulation to prevent carcinogenesis in NSCLC. BOILED-Egg plot analysis depicts that both the compounds have analogous characteristics in the divergent aspects. Moreover, in the evaluations of Blood Brain Barrier, Human Intestinal Absorption, AMES toxicity, and LD50, the virtually screened compound (PubChem CID-123449015) was found within high optimization. CONCLUSION: These investigations denote that the virtually screened compound (PubChem CID- 123449015) is more efficient to be a better prospective candidate for NSCLC treatment having good pharmacological profile than the pre-established compound CEP-37440 (PubChem CID- 71721648) with low re-rank score. The identified virtually screened compound has high potential to act as an ALK inhibitor and can show promising results in the research of non-small cell lung cancer (NSCLC).

Structure-based Virtual Screening for the Identification of High-affinity Small Molecule Towards STAT3 for the Clinical Treatment of Osteosarcoma.

BACKGROUND: According to DCEG investigation, the compared results of the osteosarcoma incidences in different continents, reported it to be the most diagnosed in adolescents and adults above 60 yrs. old. Less than 15% of patients get cured with surgery alone but the addition of chemotherapy to the treatment increases the survival rate of patient by 58%-76%. Surgical resection and aggressive chemotherapy protocols are effective to an extent but have failed to improve the 5-year overall survival rate. Indubitably, new drugs and new therapeutic targets are required to improve the outcome as well as to diminish the long-term toxicities associated with the current benchmark of treatment. STAT3 appears to be an important mediator of chemoresistance in osteosarcoma. RESULTS: Experimental evidence clearly demonstrate the disruption of STAT3 signaling which inhibits the survival and proliferation of osteosarcoma and decreases the growth of disease. This prevailing study approach is by molecular docking, virtual screening to elucidate inhibitor with superior affinity against STAT3 to have a cautious pharma profile. To rectify the best-established drug with high affinity, Mol dock algorithm is executed. The compound Sorafenib (Pub CID 216239) having high-affinity scores is subjected to another similarity search to retrieve the drugs with similar properties. The virtual screened compound with PubChem CID-44815014 as per BOILED-Egg plot reveals its high affinity. CONCLUSION: Comparative study and ADMET study both showed the compounds to have equivalent properties, whereas interestingly the virtual screened compound having PubChem CID-44815014 is seen to have the lowest rerank score. These drugs are identified to have high potential to act as STAT3 inhibitors and probably can be considered for further studies in wet lab analysis.