Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.

Introduction: Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis. Methods: By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out. Results: Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer. Conclusion: Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

Computational molecular perspectives on novel carbazole derivative as an anti-cancer molecule against CDK1 of breast and colorectal cancers via gene expression studies, novel two-way docking strategies, molecular mechanics and dynamics.

With increase in cancer incidences, alternative strategies for disease management are of utmost importance. Carbazole, is a compound that is being studied extensively as an anti-cancer compound. In this work, we aimed to investigate a carbazole derivative against specific cancer types such as breast and colorectal, based on the off-target analyses of carbazole derivative. The present work shortlisted 6 proteins that have an association in both cancer types, and then employed two different molecular docking strategies to examine the binding stability of carbazole derivative: a blind-docking state, where the pockets were undefined and mutation-docking state, where possible mutations were induced within the proteins. The results showed that CDK1 bound best in both states to carbazole derivative, and performed better than an array of positive controls. Molecular dynamic simulations at 100 ns further proved its stability, with carbazole derivative-CDK1-blind and mutated complex having RMSD values between 3.2 and 3.6 Å, and 2.8-3.2 Šrespectively. Molecular-mechanics generalized born and surface area solvation disclosed free energy of binding for the complexes as -28.79 ± 3.97 kcal/mol and -31.86 ± 5.09 kcal/mol respectively, with carbazole derivative bound stably within the binding pocket at every 10 ns of the 100 ns trajectory. Radial distribution functions showed that the bell curve was well within 6 Å, thus showing that carbazole derivative and its atoms do not deviate away from the pocket, suggesting its ability to be used as a good anti-cancer compound against breast and colorectal.

Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches.

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds ß-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. ß-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.

Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations.

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis. Toxicity predictions via Protox II, SwissADME and T.E.S.T (Toxicity Estimation Software Tool) revealed that these derivatives were non-toxic and had little to no adverse effects. Molecular docking studies carried out in Schrodinger with two quinoline derivatives (referred Q1 and Q2) docked against selected target proteins (PDB IDs: 2VAM and1FSE) of B. subtilis demonstrated ideal binding energies (2VAM-Q1: - 4.63 kcal/mol and 2VAM-Q2: - 4.46 kcal/mol, and 1FSE-Q1: - 3.51 kcal/mol, 1FSE-Q2: - 6.34 kcal/mol). These complexes were simulated at 100 ns and the outcomes revealed their stability with slight conformational changes. Anti-microbial assay via disc diffusion method revealed zones of inhibition showing that B. subtilis was inhibited by both Q1 and Q2, with Q2 performing slightly better than Q1, pointing towards its effectiveness against this organism and necessitating further study on other bacteria in prospective studies. Thus, this study demonstrates that our novel quinoline derivatives exhibit antibacterial properties against Bacillus subtilis and can act as potent anti-bacterials.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis.

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations.

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

Deciphering the interaction mechanism of natural actives against larval proteins of Aedes aegypti to identify potential larvicides: a computational biology analysis.

Aedes aegypti is the target for repellents to curb incidences of vector-borne diseases. Stopping breeding of this mosquito species at its larval stages helps in controlling spread of insect-borne diseases. Therefore, the present study focused on deciphering the mechanism of interaction of selected natural actives against larval proteins of A. aegypti to identify potential natural alternative larvicides. 65 larval proteins were identified from literature, whose structures were modelled and validated using RaptorX and ProCheck. 11 natural actives were selected for predicting their ligand properties and toxicities via Toxicity Estimation Software Tool and ProTox-II. Molecular docking studies were carried out using POAP followed by 100 ns molecular dynamic simulation studies for top three best docked complexes to better understand the robustness of docking, complex stabilities and molecular mechanisms of interactions. Toxicity predictions revealed that 6 molecules belonged to toxicity class 4, and five to toxicity class 5, implying that they were all safe to use. Complexes goniothalamin-translation elongation factor (-10 kcal/mol), andrographolide-acetyl-CoA C-myristoyltransferase (-9.2 kcal/mol) and capillin-translation elongation factor (-8.4 kcal/mol) showed best binding energies. When simulated, capillin-translation elongation factor showed most stability, while the remaining two also evidenced robust docking. Evolutionary studies for top two larval proteins disclosed 100 other insect species in which these proteins can be targeted using various larvicides. Protein-protein interaction network analysis revealed several protein pathways that might be affected due to aforesaid naturals. Therefore, this study provides computational insights into the molecular interaction of naturals against larval proteins, acting as potential natural larvicides.Communicated by Ramaswamy H. Sarma.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

Comprehensive Molecular Interaction Studies to Construe the Repellent/Kill Activity of Geraniol During Binding Event Against Aedes aegypti Proteins.

Aedes aegypti is an etiological agent for dengue, chikungunya, zika, and yellow fever viruses. With the advent of the use of natural alternatives as repellents, their precise mode of action during the event of binding is still unclear. Geraniol is one such bioactive natural that has been previously shown to have some insecticide properties. Thus, the present study aimed to understand the mechanism of the binding event of geraniol with the whole proteome of A. aegypti. Twenty protein target categories were shortlisted for the mosquito, wherein the proteins were downloaded with respect to the reference proteome. Conserved domain analysis was performed for the same using the CDD search tool to find the proteins that have common domains. 309 proteins were modeled using RaptorX standalone tool, and validated using Ramachandran plots from SAVES v6.0 from ProCheck. These modeled and validated proteins were then docked against geraniol, using POAP software, for understanding the binding energies. The top 3 best-docked complexes were then analyzed for their stabilities and event of binding via 100 ns simulation studies using DESMOND's Maestro environment. The docking results showed that the geraniol-voltage-gated sodium channel had the best energy of - 7.1 kcal/mol, followed by geraniol-glutathione-S-transferase (- 6.8 kcal/mol) and geraniol-alpha esterase (- 6.8 kcal/mol). The simulations for these 3 complexes revealed that several residues of the proteins interacted well with geraniol at a molecular level, and all three docked complexes were found to be stable when simulated (RMSD: 16-18 Å, 3.6-4.8 Å, 4.8-5.6 Å, respectively). Thus, the present study provides insights into the mechanism of the binding event of geraniol with the major A. aegypti targets, thereby, assisting the use of geraniol as a natural repellent.

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers.

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets.

Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies.

Molecular Docking and Interaction Studies of Identified Abscisic Acid Receptors in Oryza sativa: An In-Silico Perspective on Comprehending Stress Tolerance Mechanisms.

Background: Abiotic stresses affect plants in several ways and as such, phytohormones such as abscisic acid (ABA) play an important role in conferring tolerance towards these stresses. Hence, to comprehend the role of ABA and its interaction with receptors of the plants, a thorough investigation is essential. Aim: The current study aimed to identify the ABA receptors in Oryza sativa, to find the receptor that binds best with ABA and to examine the mutations present to help predict better binding of the receptors with ABA. Methods: Protein sequences of twelve PYL (Pyrabactin resistance 1) and seven PP2C (type 2C protein phosphatase) receptors were retrieved from the Rice Annotation Project database and their 3D structures were predicted using RaptorX. Protein-ligand molecular docking studies between PYL and ABA were performed using AutoDock 1.5.6, followed by 100ns molecular dynamic simulation studies using Desmond to determine the acceptable conformational changes after docking via root mean square deviation RMSD plot analysis. Protein-protein docking was then carried out in three sets: PYL-PP2Cs, PYL-ABA-PP2C and PYL(mut)-ABA-PP2C to scrutinize changes in structural conformations and binding energies between complexes. The amino acids of interest were mapped at their respective genomic coordinates using SNP-seek database to ascertain if there were any naturally occurring single nucleotide polymorphisms (SNPs) responsible for triggering rice PYLs mutations. Results: Initial protein-ligand docking studies revealed good binding between the complexes, wherein PYL6-ABA complex showed the best energy of -8.15 kcal/mol. The 100ns simulation studies revealed changes in the RMSD values after docking, indicating acceptable conformational changes. Furthermore, mutagenesis study performed at specific PYL-ABA interacting residues followed by downstream PYL(mut)-ABA-PP2C protein-protein docking results after induction of mutations demonstrated binding energy of -8.17 kcal/mol for PP2C79-PYL11-ABA complex. No naturally occurring SNPs that were responsible for triggering rice PYL mutations were identified when specific amino acid coordinates were mapped at respective genomic coordinates. Conclusion: Thus, the present study provides valuable insights on the interactions of ABA receptors in rice and induced mutations in PYL11 that can enhance the downstream interaction with PP2C.

Herbal Lead as Ideal Bioactive Compounds Against Probable Drug Targets of Ebola Virus in Comparison with Known Chemical Analogue: A Computational Drug Discovery Perspective.

Ebola is a deadly virus that has recently emerged as an enormous public health concern which causes dangerous illness with high fatality rates of 90 %. The virus is not receptive to known antivirals, and hence, there is a promising need to identify novel inhibitors to combat the disease. The present study deals with identification of potential herbal leads that probably subdue the activity of four major drug targets of Ebola virus such as VP24, VP30, VP35 and VP40 by computer-aided virtual screening. The selection of receptors was performed based on their functional roles in the disease. The drug likeliness and ADMET parameters of 150 herbal ligands were computationally predicted. Those molecules that qualified these parameters were preferred for docking studies with the protein targets. An existing chemical antiviral drug, BCX4430 was also docked and its theoretical binding energy was scrutinized. The docking studies suggested that herbal ligand Limonin demonstrated high binding properties with VP24 and VP35 (binding energy -9.7 kcal/mol). Similarly, curcumin exhibited good binding with VP30 (binding energy -9.6 kcal/mol). Further, Mahanine displayed superior interaction with VP40 (binding energy -7.7 kcal/mol). These herbal leads demonstrated better binding potential than the known chemical analogue in the computational studies. This study serves to bestow paramount information for further experimental studies concerning the utility of herbal ligands as probable lead molecules against Ebola viral targets.