In-silico Identification Research Articles

An In-Silico Identification of Potential Flavonoids against Kidney Fibrosis Targeting TGFβR-1.

Fibrosis is a hallmark of progressive kidney diseases. The overexpression of profibrotic cytokine, namely transforming growth factor β (TGF-β) due to excessive inflammation and tissue damage, induces kidney fibrosis. The inhibition of TGF-β signaling is markedly limited in experimental disease models. Targeting TGF-β signaling, therefore, offers a prospective strategy for the management of kidney fibrosis. Presently, the marketed drugs have numerous side effects, but plant-derived compounds are relatively safer and more cost-effective. In this study, TGFβR-1 was targeted to identify the lead compounds among flavonoids using various computational approaches, such as ADME/T (absorption, distribution, metabolism, and excretion/toxicity) analysis, molecular docking, and molecular dynamics simulation. ADME/T screening identified a total of 31 flavonoids with drug-like properties of 31 compounds, a total of 5 compounds showed a higher binding affinity to TGFβR-1, with Epicatechin, Fisetin, and Luteolin ranking at the top three (-13.58, -13.17, and -10.50 kcal/mol, respectively), which are comparable to the control drug linagliptin (-9.074 kcal/mol). The compounds also exhibited outstanding protein-ligand interactions. The molecular dynamic simulations revealed a stable interaction of these compounds with the binding site of TGFβR-1. These findings indicate that flavonoids, particularly Epicatechin, Fisetin, and Luteolin, may compete with the ligand-binding site of TGFβR-1, suggesting that these compounds can be further evaluated for the development of potential therapeutics against kidney fibrosis. Further, in-vitro and in-vivo studies are recommended to support the current findings.

SPERM-ZONA BINDING INTERACTION: AN IN SILICO BLUEPRINT TO IDENTIFY MARKER MOLECULES FOR EFFECTIVE FERTILIZATION

Precise binding interaction between sperm surface-specific molecules and the ovum zona-pellucida is the key to a positive fertilization outcome. An in-silico approach was therefore adopted to understand the intricate molecular docking between a receptor molecule of the Zona-pellucida (ZP3) and effective sperm ligands selected viz., IZUMO1, ADAM2 and ERp57. The study aims at identifying the possible molecular biomarkers on the sperm plasma membrane which could efficiently initiate the acrosome reaction, recognize and bind to barrier membrane molecules and mediate fusion of sperm with ovum, thus culminating in successful fertilization. The in-silico identification of sperm-oocyte interaction biomarkers would serve a dual purpose in aiding to recognise potential targets for fertility regulation and infertility research.

In-silico identification of critical residues in the mannose-transfer mechanism of phosphatidyl-myo-inositol mannosyltransferase B′

The complex cellular envelope is one of the major reasons behind the survival in hostile conditions and the emergence of the drug-resisting properties of mycobacteria. Phosphatidyl-myo-inositol hexamannoside (PIM6), Lipomannan (LM), and Lipoarabinomannan (LAM) are important structural constituents of the cell envelope and have roles in modulating host immune functions. Phosphatidyl-myo-inositol (PI) is first mannosylated at the 2-position of the inositol group by phosphatidyl-myo-inositol mannosyltransferase A (PimA) to produce phosphatidyl-myo-inositol monomannoside (PIM1). This PIM1 is then further mannosylated at the 6-position of the inositol group by phosphatidyl-myo-inositol mannosyltransferase B' (PimB′) utilizing GDP-mannose as the mannose-donor to synthesize phosphatidyl-myo-inositol dimannoside (PIM2) and GDP. Further mannosylation and acylation on PIM2 produce Ac1/2PIM4, which can then be converted to either Ac1/2PIM6 or LM/LAM. Detailed functional mechanism of how PimB′ transfers the mannose sugar to PIM1 is not understood. Using molecular docking, the interactions of PimB′ with the substrate PIM1 and the product PIM2 are analyzed here. Molecular dynamics (MD) simulations of PimB′ with the substrates and the products were performed for 300ns to find out critical residues involved in the mannose-transfer reaction. Docking and MD analyses indicated the residues R206 and R210 bind both PIM1 and PIM2 and are critical in the mannose-transfer reaction. The residues 120HEVGWSMLPGS130 and 281RTRGGGL288 were involved in the transfer of PIM1 from the active site. The residues 18IGG20, K211, E290, G291, 294IV295, and E298 were also important in the mannosylation reaction. The crucial residues obtained from this study may help design novel drugs against mycobacterial PimB'.

Genome-Wide Analysis and Expression Profiling of SlHsp70 Gene Family in Solanum lycopersicum Revealed Higher Expression of SlHsp70-11 in Roots under Cd2+ Stress.

Tomato is an important part of daily food, rich source of multitude nutrients, suitable candidate for bio-pharmaceutical production due to berry size and has numerous health benefits. Transcriptional regulation of metalloregulatory heat shock protein-70 family plays pivotal role in plants tolerance against abiotic stress factors including salinity, heat, cold, drought and trace metal elements such as cadmium (Cd2+). Here, we provide comprehensive report on in-silico identification of SlHsp70 family genes in tomato (Solanum lycopersicum) and their expression in tomato via qPCR analysis under broad range of trace metal elements. In-silico analysis revealed 23 SlHsp70 family genes in tomato, phylogenetically divided into four groups I-IV and displayed expression in all tissues. Gene Ontology (GO) analysis revealed that SlHSP70 proteins were membrane localized which were involved in metal ions translocation and oxidoreductase activity to counter hyper-accumlation of reactive oxygen species (ROS). Cd2+ is a widespread heavy metal soil contaminent which is continously polluting fertile soils, a knotty issue which has serious implications over photosynthesis, nitrogen assimilation, minerals and water absorption by plants. Plants exposure to Cd2+ and subsequent qRT-PCR analysis revealed increased expression of SlHsp70-11 in tomato roots, which can be employed in breeding low Cd2+ enriched tomato varieties.

SPERM-ZONA BINDING INTERACTION: AN IN SILICO BLUEPRINT TO IDENTIFY MARKER MOLECULES FOR EFFECTIVE FERTILIZATION

Precise binding interaction between sperm surface-specific molecules and the ovum zona-pellucida is the key to a positive fertilization outcome. An in-silico approach was therefore adopted to understand the intricate molecular docking between a receptor molecule of the Zona-pellucida (ZP3) and effective sperm ligands selected viz., IZUMO1, ADAM2 and ERp57. The study aims at identifying the possible molecular biomarkers on the sperm plasma membrane which could efficiently initiate the acrosome reaction, recognize and bind to barrier membrane molecules and mediate fusion of sperm with ovum, thus culminating in successful fertilization. The in-silico identification of sperm-oocyte interaction biomarkers would serve a dual purpose in aiding to recognise potential targets for fertility regulation and infertility research.

In-Silico Identification of Ideal Therapeutic Drug Targets and Characterization of Hypothetical Proteins from Trichophyton Verrucosum

Dermatophytosis is a potentially deadly fungal infection of the skin that can have major effects on both the patient's quality of life and the economy. The present work presents a computationally based in-silico approach for annotation of Trichophyton verrucosum hypothetical proteins and their potential therapeutic targets. Trichophyton verrucosum is a zoophilic dermatophyte fungus found worldwide. In rural areas, the fungus T. verrucosum is a common cause of human ringworm, which may be passed on either directly or indirectly from cattle to humans. There has been a significant rise in gene products due to genome sequencing projects, including Hypothetical Proteins (HP) with uncertain functions. Exploring and characterizing the actions of HPs is important in T.verrucosum, a dangerous fungus that causes disease in animals and can be transmitted to people. Many pipelines and web servers that incorporate multiple technologies are made accessible to achieve genome-wide identification of effector proteins. We have performed an NCBI search for hypothetical proteins identification. NCBI-CD and NCBI protein blast is used to identify the conserved domains and non-homologus proteins. The present research strategy will aid in structure prediction of different proteins, the discovery of binding sites, and drug design, allowing for a more in-depth examination of these proteins in wet laboratory experiments and helping in therapeutic development against T. verrucosum. In today's world, HPs might be a viable target for treating diseases linked to antibiotic resistance and drug resistance. This research of drug designing on hypothetical proteins will help farmers and animals to prevent from fungal disease.

In-silico identification of lysine residue for α-Amylase immobilization on dialdehyde cellulose

Enzymes are the precious gift of nature to humans. The wise utilization of enzymes may reduce energy needs of humans and the Immobilization technique can help a lot in this regard. This aspect overcomes limitations of the enzymes, therefore providing an opportunity to explore enzymatic chemistry further. In the present context, it is quite cumbersome & costly to identify the amino acid of enzymes involved in the covalent mode of Immobilization. In the present study, molecular modeling techniques were used to do this difficult task. The present work used molecular modeling methods to extract information about the immobilization of α-Amylase (E.C.3.2.1.1) on Dialdehyde Cellulose. The Lysine residue is the most probable residue to interact with Dialdehyde Cellulose. In the present work, a total of 23 lysine residues were used to study covalent binding behavior with α-Amylase. It was found that if Lys142 is involved in binding with Dialdehyde Cellulose then binding affinity (−6.1 & −5.9 kcal mol−1), as well as the involvement of amino acids of both free α-Amylase and Lys142 immobilized α-Amylase with the starch substrate, were found to be similar. The technique reported here is used for the identification of amino acid residue for the Immobilization of enzymes.

In-Silico Identification of Natural Compounds from Traditional Medicine as Potential Drug Leads against SARS-CoV-2 Through Virtual Screening.

The novel coronavirus strain SARS-CoV-2 is the virus responsible for the recent global health crisis, as it causes the coronavirus disease-19 (COVID-19) in humans. Due to its high rate of spreading and significant fatality rates, the situation has escalated to a pandemic, which is the cause of immense disruption in daily life. In this study, we have taken a docking-based virtual screening approach to select natural molecules (from plants) with possible therapeutic potential. For this purpose, AUTODOCK Vina-based determination of binding affinity values (blind and active-site oriented) was obtained to short-list molecules with possible inhibitory potential against the main Mpro in SARS-CoV-2 (PDB ID 6Y2F -the monomeric form). The 4 molecules selected were Chebuloside (−8.2; −8.2), Acetoside (−8.0; −8.0), Corilagin (−8.1; −7.7) and Arjunolic Acid (−8.0; −7.6) (blind and active-site oriented docking scores (Kcal/mol) in parenthesis, respectively). Further, a comparative search, with FDA-approved drugs, has shown that Ouabain was comparable to Chebuloside with a similarity score of 0.227. This in silico finding with respect to Ouabain is significant, since this polycyclic glycoside has been shown to treat COVID-19 positive patients with a cardiovascular disease. Hydrocortisone was similar to Arjunolic acid with a score of 0.539. Again, this likeness is worthy of mention, since hydrocortisone has been used earlier for the treatment of SARS-CoV1 and MERS. However, further experimentation and validation of the results, in suitable biological model systems, are necessary to gain more insight and relevance as well as provide corroborative evidence for our in-silico findings.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40011-021-01292-5.

Insilico identification and characterization of abiotic stress responsive genes in Cucumis melo L. using bioinformatics tools

Insilico identification and characterization of abiotic stress responsive genes in Cucumis melo L. using bioinformatics tools

PGN-005 - In-silico identification of novel inhibitors for glutamate racemase (MurI) to decipher the subtle of antimicrobial resistance in Neisseria gonorrhoeae: A virtual screening and molecular dynamics approach

PGN-005 - In-silico identification of novel inhibitors for glutamate racemase (MurI) to decipher the subtle of antimicrobial resistance in Neisseria gonorrhoeae: A virtual screening and molecular dynamics approach

Novel isoforms of influenza virus PA-X and PB1-F2 indicated by automatic annotation

The influenza A virus genome contains 8 gene segments encoding 10 commonly recognized proteins. Additional protein products have been identified, including PB1-F2 and PA-X. We report the in-silico identification of novel isoforms of PB1-F2 and PA-X in influenza virus genomes sequenced from avian samples. The isoform observed in PA-X includes a mutated stop codon that should extend the protein product by 8 amino acids. The isoform observed in PB1-F2 includes two nonsense mutations that should truncate the N-terminal region of the protein product and remove the entire mitochondrial targeting domain. Both isoforms were uncovered during automatic annotation of CEIRS sequence data. Nominally termed PA-X8 and PB1-F2-Cterm, both predicted isoforms were subsequently found in other annotated influenza genomes previously deposited in GenBank. Both isoforms were noticed due to discrepant annotations output by two annotation engines, indicating a benefit of incorporating multiple algorithms during gene annotation.

In Silico Identification of Potential Inhibitors of SARS-CoV-2 Papain-like Protease From Natural Sources: A Natural Weapon to Fight COVID-19

Background: The rapid spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) globally has created unprecedented health care and economic crisis. The ever-increasing death toll highlights an urgent need for the development of specific antiviral to combat Novel Coronavirus Disease 2019 (COVID-19). Objective: In the present study, we aimed to identify potential SARS-CoV-2 papain-like protease inhibitors from regularly used spices. Methods: A structure-based virtual screening (VS) of our in-house databank of 1152 compounds was employed to identify small molecule inhibitors of SARS-CoV-2 papain-like protease (PLpro), which are important protease for virus replication. The databank was built of the compounds from ten spices and two medicinal plants. Results: The top three potential hits that resulted from VS were myricetin (1) available in Alium cepa and Mentha piperita; α-hydroxyhydrocaffeic acid (2) available in M. Piperita; and luteolin (3) available in M. Piperita, Curcuma longa, A. cepa, and Trigonella foenum-graecum, which showed fair binding affinity to PLpro of SARS-CoV-2 compared to known SARS-CoV PLpro inhibitors. The predicted Absorption, Distribution, Metabolism, and Excretion (ADME) properties of the selected hits showed that all are drug-like. The compounds bind to biologically critical regions of the target protein, indicating their potential to inhibit the functionality of this component. Conclusion: There are only a few reports available in the literature on the in-silico identification of PLpro inhibitors and most of them used homology modeling of protein. Here, we used the recently uploaded X-ray crystal structure of PLpro (PDB ID: 6WX4) with a well-defined active site. Our computational approach has resulted in the identification of effective inhibitors of SARS-CoV-2PLpro. The reported edible spices may be useful against COVID-19 as a home remedy after an in- -vitro study.

Inhibitors of Plasmepsin X Plasmodium falciparum: Structure-based pharmacophore generation and molecular dynamics simulation

Malaria is a life-threatening disease caused by Plasmodium parasites, which are transmitted to humans through infected female Anopheles mosquitoes. Over the past few decades, antimalarial resistance has posed one of the most important health challenges around the globe. To counteract the increased resistance to artemisinin combination therapy (ACT), the discovery of effective new antimalarial medicines is required at the moment. Plasmepsin X (PMX), an aspartic protease, is unravelled recently for playing an important role in egress and invasion of intraerythrocytic parasites by activating subtilisin-like protease 1. Therefore, the current study is centralized on PMX for in-silico identification of effective drugs. Structure based pharmacophore generation and virtual screening filtered out 47 candidates from almost 108 million entries catered in CHEMBL, PubChem, and ZINC databases. Five best hits were chosen based on docking scores. 1 µs molecular dynamics of homologue PMX and its complexes with the shortlisted five hit molecules and a known inhibitor 49c elucidated the stability of the complexes. Consistent with docking, the binding free energy estimated using MM-PBSA method recognized hits 1 (−144.4 kJ/mol) and 2 (−130.1 kJ/mol) the best inhibitors among all hits and the known inhibitor 49c (−68.7 kJ/mol). The new inhibitors can be put forward for further in-vivo studies as an antimalarial drug based on the comprehensive analyses reported herein.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer’s or Parkinson’s diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers—the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.

In-silico identification of subunit vaccine candidates against lung cancer-associated oncogenic viruses

Globally, ~20% of cancer malignancies are associated with virus infections. Lung cancer is the most prevalent cancer and has a 10% 5-year survival rate when diagnosed at stage IV. Cancer vaccines and oncolytic immunotherapy are promising treatment strategies for better clinical outcomes in advanced-stage cancer patients. Here, we used a reverse vaccinology approach to devise subunit vaccine candidates against lung cancer-causing oncogenic viruses. Protein components (945) from nine oncogenic virus species were systematically analyzed to identify epitope-based subunit vaccine candidates. Best vaccine candidates were identified based on their predicted ability to stimulate humoral and cell-mediated immunity and avoid self-tolerance. Using a rigorous integrative approach, we identified 125 best antigenic epitopes with predicted B-cell, T-cell, and/or MHC-binding capability and vaccine adjuvant potential. Thirty-two of these antigenic epitopes were predicted to have IL-4/IFN-gamma inducing potential and IL-10 non-inducing potential and were predicted to bind 15 MHC-type I and 49 MHC-type II alleles. All 32 epitopes were non-allergenic and 31 were non-toxic. The identified epitopes showed good conservancy and likely bind a broad class of human HLA alleles, indicating promiscuous potential. The majority of best antigenic epitopes were derived from Human papillomavirus and Epstein-Barr virus proteins. Of the 32 epitopes, 25 promiscuous epitopes were related to E1 and E6 envelope genes and were present in multiple viral strains/species, potentially providing heterologous immunity. Further validating our results, 38 antigenic epitopes were also present in the largest experimentally-validated epitope resource, Immune Epitope Database and Analysis Resource. We further narrowed the selection to 29 antigenic epitopes with the highest immunogenic/immune-boosting potential. These epitopes possess tremendous therapeutic potential as vaccines against lung cancer-causing viruses and should be validated in future experiments. All findings are available at https://webs.iiitd.edu.in/raghava/vlcvirus/.

ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins

Proto-oncogenes are the genes that have the potential to transform normal cells into cancer cells as a result of mutations. They usually contain encoding of proteins whose function is to inhibit cell differentiation, stimulate cell division, and prevent the death of cells. While the prognosis regarding proto-oncogene may occur at varying phases of cancer, the accuracy of the identification method is always questionable. The standard procedure for detecting these genes involves in-vitro experimentations but it proves to be very costly, time taking, and laborious. This problem is addressed by the use of computer-aided approaches established in studies encompassing methods in computational biology and bioinformatics. Early diagnosis of cancer is crucial for the full recovery of the patient. Proto-oncogene proteins are an important biomarker that helps identify the onset of a specific type of cancer. Keeping this in mind, this study proposes an efficient methodology for in-silico identification of proto-oncogenes. The predictor proposed in this study computes position and composition relevant statistical features incorporated into the pseudo-amino-acid composition (PseAAC) based on Chou’s 5-step rules. Subsequently, the study finds that the use of a random forest classifier performs the most accurate prediction of proto-oncogene proteins. The method was validated using the 10 folds cross-validation, Jackknife testing, Self-Consistency, and Independent set testing, giving 95.44%, 97.17%, 99.8%, and 96.41% accurate results, respectively. These results imply that the proposed model can play a key role in the early prognosis of cancer and aid scientists in the discovery of mechanisms against cancer.

AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration. Though, the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides (“AIP”) are usually very costly and remain time-consuming. Therefore, before wet-lab investigations, it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process. Several anti-inflammatory prediction tools have been proposed in the recent past, yet, there is a space to induce enhancement in prediction performance in terms of precision and efficiency. An exceedingly accurate anti-inflammatory prediction model is proposed, named AntiFlamPred (“Anti-inflammatory Peptide Predictor”), by incorporation of encoded features and probing machine learning algorithms including deep learning. The proposed model performs best in conjunction with deep learning. Rigorous testing and validation were applied including cross-validation, self-consistency, jackknife, and independent set testing. The proposed model yielded 0.919 value for area under the curve (AUC) and revealed Mathew’s correlation coefficient (MCC) equivalent to 0.735 demonstrating its effectiveness and stability. Subsequently, the proposed model was also extensively probed in comparison with other existing models. The performance of the proposed model also out-performs other existing models. These outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based examinations. Subsequently, it has the potential to assiduously support medical and bioinformatics research.

Insilico Identification of Genes and Molecular Pathways during Aging in <i>Drosophila</i> Brain

The regulation of gene expression in brain vicissitudes during aging is still not much known and explored. Differential gene expression and regulation is a key factor involved to identify the important landmarks within the brain transcriptome to study neuronal aging. Recently, transcriptomic studies are highly explored to understand and depict diseased versus normal as next generation sequencing enables to capture the complete biological context to the entire genome. Study of gene expression during aging compared to young flies provides a signature and scenario of gene expression and regulation during aging. In this study, we took advantage of NGS raw data of young and old flies head from SRA database of NCBI and decrypted the gene expression regulation during normal aging in drosophila model. We identified 350 genes with significant differential expression between young and old flies having 0.01% FDR. Various pathways in context to identified genes which are involved in aging include autophagy i.e. cell death and apoptosis, proteolysis, oxidative stress, declination grey and white matter and neurotransmitter levels, mitochondrial discrepancy, electron transport chain, sugar degradation pathways, activation of transcription factors involved in epigenetic changes, regulators involved in negative and positive regulation WNT signaling pathways, G protein coupled receptor etc. as all these factors contribute to neurodegeneration and possibly dementia in normal aging. So, to find the specific genes and regulators which are differentially expressed in normal aging, we investigate brain transcriptome of normal aging flies compared to young flies which offer a repertoire of genes, regulators and factors involved in network of neurodegeneration to establish direct correlation between aging and dementia. We also identified the pathways which are involved in aging and corresponding gene regulation in these pathways in aging flies brain. It is found that there are some common pathways whose genes and regulators are highly differentially regulated in both aging and dementia.

In-silico identification of potential inhibitors targeting the DNA binding domain of estrogen receptor α for the treatment of hormone therapy-resistant breast cancer

Estrogen receptor α (ERα) plays a critical role in breast cancer (BC) development. The standard therapeutic strategies for ERα- positive (ERα+) BC consist of impairing ERα signalling pathway by either estrogen competitors blocking its interaction with the ligand binding domain (LBD) or agents inhibiting the production of estrogen. These strategies are limited by many factors that lead to constitutive activation of ERα and consequently, resistance to treatment. Targeting the DNA binding domain (DBD) of ERα instead of its LBD with small-molecule inhibitors could be an alternative to impair ERα’s signalling pathway. For this purpose, we conducted a structure based virtual screening of DrugBank against the crystal structure of ERα-DBD (PDB ID: 1HCQ) using the Glide module in standard precision (SP) and extra precision (XP) mode of docking. Molecules with XP Gscore less than −8 kcal/mol were selected and visually inspected to keep only the reasonable docking poses. Subsequently, these molecules were clustered using structural interaction fingerprints analysis and the complexes of the top ranked molecules of each cluster based on XP Gscore were subjected to 200 ns molecular dynamics simulations followed by MM-GBSA binding free energy calculation for the last 100 ns of each complex. In this study, we identified three molecules from DrugBank namely DB03450, DB02593 and DB08001 showing significant stability and strong interaction with the key amino acids during MD simulation suggesting a potential inhibition of the target. These molecules could be used as promising lead compounds to impair the ERα signalisation in hormone therapy-resistant breast cancer. Communicated by Ramaswamy H. Sarma

In-silico identification of new inhibitors for Low-density lipoprotein receptor-related protein6 (LRP6)

Low-density lipoprotein receptor-related protein 6 (LRP6) is an important therapeutic target for diseases such as osteoporosis, Alzheimer, cancer, and neurodegenerative disease. Computational methods such as ligand-based and structure-based virtual screening have been introduced as an extremely efficient and accurate tool for finding new drug targets and candidates. In this study, we aimed to screen the National Cancer Institute (NCI) Diversity Set II and parts of the ZINC database by virtual screening to identify potential and safe compounds that can inhibit the LRP6 protein. By utilizing various screening methods such as rigid and flexible molecular docking and Lipinski’s rule of five, we identified 10 potential compounds. Then, their validity was further tested by molecular dynamics simulation and MMPBSA binding free energy calculations. Eventually, it was concluded that ZINC03954520, ZINC01729523, ZINC03898665, ZINC13152226, ZINC26730911 and ZINC01069082 compounds can be potential drug compounds for inhibiting LRP6 protein. These compounds in complex with β-propeller domains of LRP6 showed that they are capable of altering the backbone of these domains and interfere with their structural dynamics which may lead to the inhibition of the signal transmission. Communicated by Ramaswamy H. Sarma