Revelation of enzyme activity of mutant pyrazinamidases from Mycobacterium tuberculosis upon binding with various metals using quantum mechanical approach

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

BACKGROUND

1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.

OBJECTIVE

Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.

METHODOLOGY

The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.

RESULTS

The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.

CONCLUSION

For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .

iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models

Background

Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification.

Objective

The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors.

Methods

The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing.

Results

The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors.

Conclusion

The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/.

LBCEPred: a machine learning model to predict linear B-cell epitopes

B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.

In silico study of some selective phytochemicals against a hypothetical SARS-CoV-2 spike RBD using molecular docking tools

Background

This world is currently witnessing a pandemic outbreak of ‘COVID-19′ caused by a positive-strand RNA virus ‘SARS-CoV-2’. Millions have succumbed globally to the disease, and the numbers are increasing day by day. The viral genome enters into the human host through interaction between the spike protein (S) and host angiotensin-converting enzyme-2 (ACE2) proteins. S is the common target for most recently rolled-out vaccines across regions. A recent surge in single/multiple mutations in S region is of great concern as it may escape vaccine induced immunity. So far, the treatment regime with repurposed drugs has not been too successful.

Hypothesis

Natural compounds are capable of targeting mutated spike protein by binding to its active site and destabilizing the spike-host ACE2 interaction.

Materials and methods

A hypothetical mutated spike protein was constructed by incorporating twelve different mutations from twelve geographical locations simultaneously into the receptor-binding domain (RBD) and docked with ACE2 and seven phytochemicals namely allicin, capsaicin, cinnamaldehyde, curcumin, gingerol, piperine and zingeberene. Molecular Dynamic (MD) simulation and Principal Component Analysis (PCA) were finally used for validation of the docking results.

Result

The docking results showed that curcumin and piperine were most potent to bind ACE2, mutated spike, and mutated spike-ACE2 complex, thereby restricting viral entry. ADME analysis also proved their drug candidature. The docking complexes were found to be stable by MD simulation.

Conclusion

This result provides a significant insight about the phytochemicals' role, namely curcumin and piperine, as the potential therapeutic entities against mutated spike protein of SARS-CoV-2.

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.

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.

Biological perspective of thiazolide derivatives against Mpro and MTase of SARS-CoV-2: Molecular docking, DFT and MD simulation investigations

Humans around the globe have been severely affected by SARS-CoV-2 and no treatment has yet been authorized for the treatment of this severe condition brought by COVID-19. Here, an in silico research was executed to elucidate the inhibitory potential of selected thiazolides derivatives against SARS-CoV-2 Protease (Mpro) and Methyltransferase (MTase). Based on the analysis; 4 compounds were discovered to have efficacious and remarkable results against the proteins of the interest. Primarily, results obtained through this study not only allude these compounds as potential inhibitors but also pave the way for in vivo and in vitro validation of these compounds.

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.

Prediction of Mycobacterium tuberculosis pyrazinamidase function based on structural stability, physicochemical and geometrical descriptors

BACKGROUND

Pyrazinamide is an important drug against the latent stage of tuberculosis and is used in both first- and second-line treatment regimens. Pyrazinamide-susceptibility test usually takes a week to have a diagnosis to guide initial therapy, implying a delay in receiving appropriate therapy. The continued increase in multi-drug resistant tuberculosis and the prevalence of pyrazinamide resistance in several countries makes the development of assays for prompt identification of resistance necessary. The main cause of pyrazinamide resistance is the impairment of pyrazinamidase function attributed to mutations in the promoter and/or pncA coding gene. However, not all pncA mutations necessarily affect the pyrazinamidase function.

OBJECTIVE

To develop a methodology to predict pyrazinamidase function from detected mutations in the pncA gene.

METHODS

We measured the catalytic constant (kcat), KM, enzymatic efficiency, and enzymatic activity of 35 recombinant mutated pyrazinamidase and the wild type (Protein Data Bank ID = 3pl1). From all the 3D modeled structures, we extracted several predictors based on three categories: structural stability (estimated by normal mode analysis and molecular dynamics), physicochemical, and geometrical characteristics. We used a stepwise Akaike's information criterion forward multiple log-linear regression to model each kinetic parameter with each category of predictors. We also developed weighted models combining the three categories of predictive models for each kinetic parameter. We tested the robustness of the predictive ability of each model by 6-fold cross-validation against random models.

RESULTS

The stability, physicochemical, and geometrical descriptors explained most of the variability (R2) of the kinetic parameters. Our models are best suited to predict kcat, efficiency, and activity based on the root-mean-square error of prediction of the 6-fold cross-validation.

CONCLUSIONS

This study shows a quick approach to predict the pyrazinamidase function only from the pncA sequence when point mutations are present. This can be an important tool to detect pyrazinamide resistance.

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.