In Silico Inhibition of BACE-1 by Selective Phytochemicals as Novel Potential Inhibitors: Molecular Docking and DFT Studies

The beneficial pharmacological effects of Uncaria rhynchophylla in neurodegenerative diseases: focus on alkaloids

With the intensification of aging population, the prevention or treatment of neurodegenerative diseases, such as Parkinson's disease and Alzheimer's disease, has drawn more and more attention. As a long used traditional Chinese medicine, Uncaria rhynchophylla (Miq.) Jacks., named Gouteng in Chinese, has been reported to have an effective neuroprotective role in neurodegenerative diseases. In this review, the beneficial pharmacological effects and signaling pathways of herbal formulas containing U. rhynchophylla, especially major compounds identified from U. rhynchophylla, such as corynoxine B, corynoxine, rhynchophylline, and isorhynchophylline, in neurodegenerative diseases, were summarized, which not only provide an overview of U. rhynchophylla for the prevention or treatment of neurodegenerative diseases but also give some perspective to the development of new drugs from traditional Chinese medicine.

Prioritization of bioactive compounds envisaging yohimbine as a multi targeted anticancer agent: insight from molecular docking and molecular dynamics simulation

Recently, multi-targeted drugs have attracted much attention in cancer therapy where several therapeutic proteins are targeted by a single agent. Using the published scientific literature, we selected sixteen well-known anticancer targets and seven potential phytobioactive chemicals to find a multitargeted compound by screening through molecular docking. The feasible protein-ligand interaction was further predicted by protein-ligand interaction analysis and molecular dynamic simulation. The phytochemical yohimbine exhibited the lowest docking score in the range of -8.3 to -10.0 kcal/mol over other ligands with all the studied protein targets. Molecular interaction data also revealed the feasible binding of yohimbine with all targets. Moreover, the molecular simulation data also confirmed the stability of protein-ligand complexes with three most scored targets viz. ERK2, PARP1 and PIK3α. Based on our results, yohimbine seems to be the most potent compound out of those selected compounds and can be considered as effective lead molecule against the studied target proteins.Communicated by Ramaswamy H. Sarma.

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 β-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave β-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of β-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.

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

Analysis of Inhibitor Binding Combined with Reactivity Studies to Discover the Potentially Inhibiting Phytochemicals Targeting Chikungunya Viral Replication

BACKGROUND

Chikungunya fever is a challenging threat to human health in various parts of the world nowadays. Many attempts have been made for developing an effective drug against this viral disease and no effective antiviral treatment has been developed to control the spread of the Chikungunya virus (CHIKV) in humans.

OBJECTIVE

This research is aimed at the discovery of potential inhibitors against this virus by employing computational techniques to study the interactions between non-structural proteins of Chikungunya virus and phytochemicals from plants.

METHODS

Four non-structural proteins were docked with 2035 phytochemicals from various plants. The ligands having binding energies ≥ -8.0 kcal/mol were considered as potential inhibitors for these proteins. ADMET studies were also performed to analyze different pharmacological properties of these docked compounds and to further analyze the reactivity of these phytochemicals against CHIKV, DFT analysis was carried out based on HOMO and LUMO energies.

RESULTS

By analyzing the binding energies, Ki, ADMET properties and band energy gaps, it was observed that 13 phytochemicals passed all the criteria to be a potent inhibitor against CHIKV in humans.

CONCLUSION

A total of 13 phytochemicals were identified as potent inhibiting candidates, which can be used against the Chikungunya virus.

Virtual Screening of Phytochemicals by Targeting HR1 Domain of SARS-CoV-2 S Protein: Molecular Docking, Molecular Dynamics Simulations, and DFT Studies

The recent COVID-19 pandemic has impacted nearly the whole world due to its high morbidity and mortality rate. Thus, scientists around the globe are working to find potent drugs and designing an effective vaccine against COVID-19. Phytochemicals from medicinal plants are known to have a long history for the treatment of various pathogens and infections; thus, keeping this in mind, this study was performed to explore the potential of different phytochemicals as candidate inhibitors of the HR1 domain in SARS-CoV-2 spike protein by using computer-aided drug discovery methods. Initially, the pharmacological assessment was performed to study the drug-likeness properties of the phytochemicals for their safe human administration. Suitable compounds were subjected to molecular docking to screen strongly binding phytochemicals with HR1 while the stability of ligand binding was analyzed using molecular dynamics simulations. Quantum computation-based density functional theory (DFT) analysis was constituted to analyze the reactivity of these compounds with the receptor. Through analysis, 108 phytochemicals passed the pharmacological assessment and upon docking of these 108 phytochemicals, 36 were screened passing a threshold of -8.5 kcal/mol. After analyzing stability and reactivity, 5 phytochemicals, i.e., SilybinC, Isopomiferin, Lycopene, SilydianinB, and Silydianin are identified as novel and potent candidates for the inhibition of HR1 domain in SARS-CoV-2 spike protein. Based on these results, it is concluded that these compounds can play an important role in the design and development of a drug against COVID-19, after an exhaustive in vitro and in vivo examination of these compounds, in future.

Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule

Background:

Allergens are antigens that can stimulate an atopic type I human hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally allergenic than others. The challenge for toxicologists is to identify properties that allow proteins to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very critical and pivotal task. The experimental identification of protein functions is a hectic, laborious and costly task; therefore, computer scientists have proposed various methods in the field of computational biology and bioinformatics using various data science approaches. Objectives: Herein, we report a novel predictor for the identification of allergen proteins.

Methods:

For feature extraction, statistical moments and various position-based features have been incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a neural network.

Results:

The predictor is validated through 10-fold cross-validation and Jackknife testing, which gave 99.43% and 99.87% accurate results.

Conclusions:

Thus, the proposed predictor can help in predicting the Allergen proteins in an efficient and accurate way and can provide baseline data for the discovery of new drugs and biomarkers.

Phytochemicals from Selective Plants Have Promising Potential against SARS-CoV-2: Investigation and Corroboration through Molecular Docking, MD Simulations, and Quantum Computations

Coronaviruses have been reported previously due to their association with the severe acute respiratory syndrome (SARS). After SARS, these viruses were known to be causing Middle East respiratory syndrome (MERS) and caused 35% evanescence amid victims pursuing remedial care. Nowadays, beta coronaviruses, members of Coronaviridae, family order Nidovirales, have become subjects of great importance due to their latest pandemic originating from Wuhan, China. The virus named as human-SARS-like coronavirus-2 contains four structural as well as sixteen nonstructural proteins encoded by single-stranded ribonucleic acid of positive polarity. As there is no vaccine available to treat the infection caused by these viruses, there is a dire need for taking necessary steps against this virus. Herein, we have targeted two nonstructural proteins of SARS-CoV-2, namely, methyltransferase (nsp16) and helicase (nsp13), respectively, due to their substantial activity in viral pathogenesis. A total of 2035 compounds were analyzed for their pharmacokinetics and pharmacological properties. The screened 108 compounds were docked against both targeted proteins and were compared with previously reported known compounds. Compounds with high binding affinity were analyzed for their reactivity through DFT analysis, and binding was analyzed using molecular dynamics simulations. Through the analyses performed in this study, it is concluded that EryvarinM, Silydianin, Osajin, and Raddeanine can be considered potential inhibitors for MTase, while TomentodiplaconeB, Osajin, Sesquiterpene Glycoside, Rhamnetin, and Silydianin for helicase after these compounds are validated thoroughly using in vitro and in vivo protocols.

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD

BACKGROUND

Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS.

OBJECTIVES

This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article.

CONCLUSION

By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

At the end of December 2019, a novel strain of coronavirus, given the name of 2019-nCoV, emerged for exhibiting symptoms of severe acute respiratory syndrome. The virus is spreading rapidly in China and around the globe, affecting thousands of people leading to a pandemic. To control the mortality rate associated with the 2019-nCoV, prompt steps are needed. Until now there is no effective treatment or drug present to control its life-threatening effects in the humans. The scientist is struggling to find new inhibitors of this deadly virus. In this study, to identify the effective inhibitor candidates against the main protease (Mpro) of 2019-nCoV, computational approaches were adopted. Phytochemicals having immense medicinal properties as ligands were docked against the Mpro of 2019-nCoV to study their binding properties. ADMET and DFT analyses were also further carried out to analyze the potential of these phytochemicals as an effective inhibitor against Mpro of 2019-nCoV.

A Sequence-Based Predictor of Zika Virus Proteins Developed by Integration of PseAAC and Statistical Moments

BACKGROUND

ZIKV has been a well-known global threat, which hits almost all of the American countries and posed a serious threat to the entire globe in 2016. The first outbreak of ZIKV was reported in 2007 in the Pacific area, followed by another severe outbreak, which occurred in 2013/2014 and subsequently, ZIKV spread to all other Pacific islands. A broad spectrum of ZIKV associated neurological malformations in neonates and adults has driven this deadly virus into the limelight. Though tremendous efforts have been focused on understanding the molecular basis of ZIKV, the viral proteins of ZIKV have still not been studied extensively.

OBJECTIVES

Herein, we report the first and the novel predictor for the identification of ZIKV proteins.

METHODS

We have employed Chou's pseudo amino acid composition (PseAAC), statistical moments and various position-based features.

RESULTS

The predictor is validated through 10-fold cross-validation and Jackknife testing. In 10- fold cross-validation, 94.09% accuracy, 93.48% specificity, 94.20% sensitivity and 0.80 MCC were achieved while in Jackknife testing, 96.62% accuracy, 94.57% specificity, 97.00% sensitivity and 0.88 MCC were achieved.

CONCLUSION

Thus, ZIKVPred-PseAAC can help in predicting the ZIKV proteins efficiently and accurately and can provide baseline data for the discovery of new drugs and biomarkers against ZIKV.

iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC

Proteases are a type of enzymes, which perform the process of proteolysis. Proteolysis normally refers to protein and peptide degradation which is crucial for the survival, growth and wellbeing of a cell. Moreover, proteases have a strong association with therapeutics and drug development. The proteases are classified into five different types according to their nature and physiochemical characteristics. Mostly the methods used to differentiate protease from other proteins and identify their class requires a clinical test which is usually time-consuming and operator dependent. Herein, we report a classifier named iProtease-PseAAC (2L) for identifying proteases and their classes. The predictor is developed employing the flow of 5-step rule, initiating from the collection of benchmark dataset and terminating at the development of predictor. Rigorous verification and validation tests are performed and metrics are collected to calculate the authenticity of the trained model. The self-consistency validation gives the 98.32% accuracy, for cross-validation the accuracy is 90.71% and jackknife gives 96.07% accuracy. The average accuracy for level-2 i.e. protease classification is 95.77%. Based on the above-mentioned results, it is concluded that iProtease-PseAAC (2L) has the great ability to identify the proteases and their classes using a given protein sequence.