Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease.

Alzheimer's disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of ß-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave ß-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of ß-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R 2 = 0.82, Q 2 loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = -8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate's inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.

Identification of potential edible mushroom as SARS-CoV-2 main protease inhibitor using rational drug designing approach.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is highly pathogenic to humans and has created health care threats worldwide. This urgent situation has focused the researchers worldwide towards the development of novel vaccine or small molecule therapeutics for SARS-CoV-2. Although several vaccines have already been discovered and are in use for the masses, no therapeutic medication has yet been approved by FDA for the treatment of COVID-19. Keeping this in view, in the present study, we have identified promising hits against the main protease (Mpro) of SARS-CoV-2 from edible mushrooms. Structure-based virtual screening (VS) of 2433 compounds derived from mushrooms was performed with Mpro protein (6LU7). Four promising hits, namely, Kynapcin-12 (M_78), Kynapcin-28 (M_82), Kynapcin-24 (M_83), and Neonambiterphenyls-A (M_366) were identified based on the result of docking, Lipinski's rule, 100 ns molecular dynamics (MD) simulation and MM/PBSA binding free energy calculations. Finally, the inhibitory properties of these hits were compared with three known inhibitors, baicalein (1), baicalin (2), and biflavonoid (3). Data indicated that M_78, M_82 and M_83 compounds present in edible mushroom Polyozellus multiplex were potent inhibitors of Mproprotein (6LU7). It could be concluded that edible mushroom Polyozellus multiplex has potential activity against SARS-CoV-2 infection and identified molecules could be further explored as therapeutic inhibitors against SARS-CoV-2.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity.

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations.

SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, Mpro (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of Mpro using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using Mpro protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with Mpro protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for Mpro, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.

Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis.

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure-Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole-indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study.

In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 â€‹= â€‹0.80-0.82, Q2 loo â€‹= â€‹0.74-0.77, Q 2 LMO  â€‹= â€‹0.66-0.67). The developed QSAR models identified number of sp2 hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.