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 design of prospective molecular targets for Burkholderia cepacia complex by molecular docking and dynamic simulation studies.

The study aimed to screen prospective molecular targets of BCC and potential natural lead candidates as effective binders by computational modeling, molecular docking, and dynamic (MD) simulation studies. Based on the virulent functions, tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein (mnmC) and pyrimidine/purine nucleoside phosphorylase (ppnP) were selected as the prospective molecular targets. In the absence of experimental data, the three-dimensional (3D) structures of these targets were computationally predicted. After a thorough literature survey and database search, the drug-likeness, and pharmacokinetic properties of 70 natural molecules were computationally predicted and the effectual binding of the best lead molecules against both the targets was predicted by molecular docking. The stabilities of the best-docked complexes were validated by MD simulation and the binding energy calculations were carried out by MM-GBSA approaches. The present study revealed that the hypothetical models of mnmC and ppnP showed stereochemical accuracy. The study also showed that among 70 natural compounds subjected to computational screening, Honokiol (3',5-Di(prop-2-en-1-yl) [1,1'-biphenyl]-2,4'-diol) present in Magnolia showed ideal drug-likeness, pharmacokinetic features and showed effectual binding with mnmC and ppnP (binding energies -7.3 kcal/mol and -6.6 kcal/mol, respectively). The MD simulation and GBSA calculation studies showed that the ligand-protein complexes stabilized throughout tMD simulation. The present study suggests that Honokiol can be used as a potential lead molecule against mnmC and ppnP targets of BCC and this study provides insight into further experimental validation for alternative lead development against drug resistant BCC.

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein.

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

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.

Transcriptomic responses under combined bacterial blight and drought stress in rice reveal potential genes to improve multi-stress tolerance.

BACKGROUND: The unprecedented drought and frequent occurrence of pathogen infection in rice is becoming more due to climate change. Simultaneous occurrence of stresses lead to more crop loss. To cope up multiple stresses, the durable resistant cultivars needs to be developed, by identifying relevant genes from combined biotic and abiotic stress exposed plants. RESULTS: We studied the effect of drought stress, bacterial leaf blight disease causing Xanthomonas oryzae pv. oryzae (Xoo) pathogen infection and combined stress in contrasting BPT5204 and TN1 rice genotypes. Mild drought stress increased Xoo infection irrespective of the genotype. To identify relevant genes that could be used to develop multi-stress tolerant rice, RNA sequencing from individual drought, pathogen and combined stresses in contrasting genotypes has been developed. Many important genes are identified from resistant genotype and diverse group of genes are differentially expressed in contrasting genotypes under combined stress. Further, a meta-analysis from individual drought and Xoo pathogen stress from public domain data sets narrowed- down candidate differentially expressed genes. Many translation associated genes are differentially expressed suggesting their extra-ribosomal function in multi-stress adaptation. Overexpression of many of these genes showed their relevance in improving stress tolerance in rice by different scientific groups. In combined stress, many downregulated genes also showed their relevance in stress adaptation when they were over-expressed. CONCLUSIONS: Our study identifies many important genes, which can be used as molecular markers and targets for genetic manipulation to develop durable resistant rice cultivars. Strategies should be developed to activate downregulated genes, to improve multi-stress tolerance in plants.

Mycobacterium Time-Series Genome Analysis Identifies AAC2' as a Potential Drug Target with Naloxone Showing Potential Bait Drug Synergism.

The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial resistance. Anti-microbial resistance has a great impact on the economic stability of the global healthcare sector. We performed a timeline genomic analysis from 2003 to 2021 of 578 mycobacterium genomes to understand the pattern underlying genomic variations. Potential drug targets based on functional annotation was subjected to pharmacophore-based screening of FDA-approved phyto-actives. Reaction search, MD simulations, and metadynamics studies were performed. A total of 4,76,063 mutations with a transition/transversion ratio of 0.448 was observed. The top 10 proteins with the least number of mutations were high-confidence drug targets. Aminoglycoside 2'-N-acetyltransferase protein (AAC2'), conferring resistance to aminoglycosides, was shortlisted as a potential drug target based on its function and role in bait drug synergism. Gentamicin-AAC2' binding pose was used as a pharmacophore template to screen 10,570 phyto-actives. A total of 66 potential hits were docked to obtain naloxone as a lead-active with a docking score of -6.317. Naloxone is an FDA-approved drug that rapidly reverses opioid overdose. This is a classic case of a repurposed phyto-active. Naloxone consists of an amine group, but the addition of the acetyl group is unfavorable, with a reaction energy of 612.248 kcal/mol. With gentamicin as a positive control, molecular dynamic simulation studies were performed for 200 ns to check the stability of binding. Metadynamics-based studies were carried out to compare unbinding energy with gentamicin. The unbinding energies were found to be -68 and -74 kcal/mol for naloxone and gentamycin, respectively. This study identifies naloxone as a potential drug candidate for a bait drug synergistic approach against Mycobacterium tuberculosis.

Design of Novel Coumarin Derivatives as NUDT5 Antagonists That Act by Restricting ATP Synthesis in Breast Cancer Cells.

Breast cancer, a heterogeneous disease, is among the most frequently diagnosed diseases and is the second leading cause of death due to cancer among women after lung cancer. Phytoactives (plant-based derivatives) and their derivatives are safer than synthetic compounds in combating chemoresistance. In the current work, a template-based design of the coumarin derivative was designed to target the ADP-sugar pyrophosphatase protein. The novel coumarin derivative (2R)-2-((S)-sec-butyl)-5-oxo-4-(2-oxochroman-4-yl)-2,5-dihydro-1H-pyrrol-3-olate was designed. Molecular docking studies provided a docking score of -6.574 kcal/mol and an MM-GBSA value of -29.15 kcal/mol. Molecular dynamics simulation studies were carried out for 500 ns, providing better insights into the interaction. An RMSD change of 2.4 Å proved that there was a stable interaction and that there was no conformational change induced to the receptor. Metadynamics studies were performed to calculate the unbinding energy of the principal compound with NUDT5, which was found to be -75.171 kcal/mol. In vitro validation via a cytotoxicity assay (MTT assay) of the principal compound was carried out with quercetin as a positive control in the MCF7 cell line and with an IC50 value of 55.57 (+/-) 0.7 µg/mL. This work promoted the research of novel natural derivatives to discover their anticancer activity.

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.

Quantum synergy in peptide folding: A comparative study of CVaR-variational quantum eigensolver and molecular dynamics simulation.

One of the technological fields that is developing the fastest is quantum computing in biology. One of the main problems is protein folding, which calls for precise, effective algorithms with fast computing times. Mapping the least energy conformation state of proteins with disordered areas requires enormous computing resources. The current study uses quantum algorithms, such as the Variational Quantum Eigensolver (VQE), to estimate the lowest energy value of 50 peptides, each consisting of seven amino acids. To determine the ground state energy value, Variational Quantum Optimisation (VQE) is first utilised to generate the energy values along with Conditional Value at Risk (CVaR) as an aggregation function is applied over 100 iterations of 500,000 shots each. This is contrasted with 50 millisecond molecular dynamics-based simulations to determine the energy levels and folding pattern. In comparison to MD-based simulations, the results point to CvaR-VQE producing more effective folding outcomes with respect to sampling and global optimization. Protein folding can be solved to get deep insights into biological processes and drug formulation with improving quantum technology and algorithms.

Disaggregation of amyloid-beta fibrils via natural metabolites using long timescale replica exchange molecular dynamics simulation studies.

CONTEXT: Amyloid fibrils are self-assembled fibrous protein aggregates that are associated with several presently incurable diseases such as Alzheimer's. disease that is characterized by the accumulation of amyloid fibrils in the brain, which leads to the formation of plaques and the death of brain cells. Disaggregation of amyloid fibrils is considered a promising approach to cure Alzheimer's disease. The mechanism of amyloid fibril formation is complex and not fully understood, making it difficult to develop drugs that can target the process. Diacetonamine and cystathionine are potential lead compounds to induce disaggregation of amyloid fibrils. METHODS: In the current research, we have used long timescale molecular simulation studies and replica exchange molecular dynamics (REMD) for 1000 ns (1 µs) to examine the mechanisms by which natural metabolites can disaggregate amyloid-beta fibrils. Molecular docking was carried out using Glide and with prior protein minimization and ligand preparation. We focused on a screening a database of natural metabolites, as potential candidates for disaggregating amyloid fibrils. We used Desmond with OPLS 3e as a force field. MM-GBSA calculations were performed. Blood-brain barrier permeability, SASA, and radius of gyration parameters were calculated.

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.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition.

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase. The research employed high-throughput virtual screening and QSAR analysis were conducted to identify compounds with optimal drug-like properties, emphasizing adherence to ADMET parameters for further evaluation. Molecular dynamics simulations to analyze the binding interactions between Papaveroline and Staurosporine with Fyn Tyrosine Kinase over a 200-ns period. The study revealed detailed insights into the binding mechanisms and stability of the Papaveroline-Fyn complex, showcasing the compound's potential as an inhibitor of Fyn Tyrosine Kinase. Comparative analysis with natural compounds and a reference compound highlighted Papaveroline's unique characteristics and promising therapeutic implications for AD treatment. Overall, the findings underscore Papaveroline's potential as a valuable drug candidate for targeting Fyn Tyrosine Kinase in AD therapy, offering new avenues for drug discovery in neurodegenerative diseases. This study contributes to advancing our understanding of molecular interactions in AD pathogenesis and paves the way for further research and development in this critical area.

Unraveling the regulatory landscape of Parkinson disease: A molecular symphony of miRNAs, transcription factors, and high-risk genes.

MicroRNAs (miRNAs) have emerged as critical regulators of post-transcriptional gene expression, impacting various biological processes (development, differentiation, and progression). In medicine, miRNAs are promising diagnostic biomarkers for neurodegenerative diseases, including Parkinson's disease (PD). The current study aims at exploring the role of miRNAs and transcription factors (TFs) in regulating genes-associated with PD. Deploying bioinformatics tools, the study identifies specific miRNAs and TFs involved in PD and their potential connections to the organ-disease junction. Notably, certain miRNAs are found to be highly expressed in brain, than compared to blood. Furthermore, the study explores the expression patterns of PD-related genes in different regions of the brain and attempts to construct complex network of interactions contributing to PD pathogenesis. Additionally, the regulatory relationship of two miRNAs namely hsa-miR-375-3p and hsa-miR-423-3p with TFs are well examined. Overall, the study provides a comprehensive moon-shot view of the molecular aspects of PD and their potential therapeutic targets which could be further used as diagnostic biomarkers in early detection, drug design and development attributing towards precision medicine.

Unified Aedes aegypti Protein Resource Database (UAAPRD): An Integrated High-Throughput In Silico Platform for Comprehensive Protein Structure Modeling and Functional Target Analysis to Enhance Vector Control Strategies.

A comprehensive examination of Aedes aegypti's proteome to detect key proteins that can be targeted with small molecules can disrupt blood feeding and disease transmission. However, research currently only focuses on finding repellent-like compounds, limiting studies on identifying unexplored proteins in its proteome. High-throughput analysis generates vast amounts of data, raising concerns about accessibility and usability. Establishing a dedicated database is a solution, centralizing information on identified proteins, functions, and modeled structures for easy access and research. This study focuses on scrutinizing key proteins in A. aegypti, modeling their structures using RaptorX standalone tool, identification of druggable binding sites using BiteNet, validating the models via Ramachandran plot studies and refining them via 50-ns molecular dynamic simulations using Schrodinger Maestro. By analyzing ~ 18 k proteins in the proteome of A. aegypti in our previous studies, all proteins involved in the light and dark circadian rhythm of the mosquito, inclusive of proteins in blood feeding, metabolism, etc. were chosen for the current study. The outcome is UAAPRD, a unique repository housing information on hundreds of previously unmodeled and un-simulated mosquito proteins. This robust MYSQL database ( https://uaaprd.onrender.com/user ) houses data on 309 modeled & simulated proteins of A. aegypti. It allows users to obtain protein data, view evolutionary analysis data of the protein categories, visualize proteins of interest, and send request to screen against the pharmacophore models present in UAAPRD against ligand of interest. This study offers crucial insights for developing targeted studies, which will ultimately contribute to more effective vector control strategies.

Multifaceted computational profiling of thymol and geraniol against the human proteome for bio-repellent alternatives: Toxicity predictions, degradation analysis, and quantum mechanical approaches.

With growing interest in natural compounds as alternative mosquito repellents, assessing the toxicity and structure of potential repellent naturals like thymol (monoterpene phenol) and geraniol (monoterpene alcohol) is vital for understanding their stability and human impact. This study aimed to determine the structural, toxicity, and binding profiles of thymol and geraniol using computational predictions, xTB metadynamics, quantum mechanics, and principal component analysis. Toxicity studies using Protox-II, T.E.S.T, and SwissADME indicated that thymol and geraniol belong to toxicity class 4 and 5, respectively, with low toxicity predictions in other endpoints. Overall pharmacokinetic profile was generated via pkCSM. Off-target predictions via SwissTarget Predictions, LigTMap, Pharmapper, and SuperPred showed that these molecules can bind to 614 human proteins. The degradation of thymol and geraniol were performed using xTB metadynamics and the outcomes showed that the degradants for both compounds were stable and had lower toxicity profile. Nine tautomers were generated via quantum mechanics for thymol and four for geraniol, with RMSD ranging from 3.8 to 6.3 Å for thymol and 3.6 to 4 Å for geraniol after superimpositions. DFT studies found that HOMO-LUMO values and electronegativity parameters of thymol and geraniol did not differ significantly from their isomers. Binding affinity studies against 614 proteins, analysed via PCA and violin plots, highlighted the probable range of binding. These multifaceted in-silico findings corroborate the stability and potential utility of thymol and geraniol as safer alternatives in repellent applications.

Screening of bioactive compounds from selected mushroom species against putative drug targets in Mycobacterium tuberculosis: a multi-target approach.

Mycobacterium tuberculosis (Mtb) is a notorious pathogen that causes one of the highest mortalities globally. Due to a pressing demand to identify novel therapeutic alternatives, the present study aims to focus on screening the putative drug targets and prioritizing their role in antibacterial drug development. The most vital proteins involved in the Biotin biosynthesis pathway and the Lipoarabinomannan (LAM) pathway such as biotin synthase (bioB) and alpha-(1->6)-mannopyranosyltransferase A (mptA) respectively, along with other essential virulence proteins of Mtb were selected as drug targets. Among these, the ones without native structures were modelled and validated using standard bioinformatics tools. Further, the interactions were performed with naturally available lead molecules present in selected mushroom species such as Agaricus bisporus, Pleurotus djamor, Hypsizygus ulmarius. Through Gas Chromatography-Mass Spectrometry (GC-MS), 15 bioactive compounds from the methanolic extract of mushrooms were identified. Further, 4 were selected based on drug-likeness and pharmacokinetic screening for molecular docking analysis against our prioritized targets wherein Benz[e]azulene from Pleurotus djamor illustrated a good binding affinity with a LF rank score of -9.036 kcal mol -1 against nuoM (NADH quinone oxidoreductase subunit M) and could be used as a prospective candidate in order to combat Tuberculosis (TB). Furthermore, the stability of the complex are validated using MD Simulations and subsequently, the binding free energy was calculated using MM-GBSA analysis. Thus, the current in silico analysis suggests a promising role of compounds extracted from mushrooms in tackling the TB burden.Communicated by Ramaswamy H. Sarma.

Computational targeting of iron uptake proteins in Covid-19 induced mucormycosis to identify inhibitors via molecular dynamics, molecular mechanics and density function theory studies.

Mucormycosis is a concerning invasive fungal infection with difficult diagnosis, high mortality rates, and limited treatment options. Iron availability is crucial for fungal growth that causes this disease. This study aimed to computationally target iron uptake proteins in Rhizopus arrhizus, Lichtheimia corymbifera, and Mucor circinelloides to identify inhibitors, thereby halting fungal growth and intervening in mucormycosis pathogenesis. Seven important iron uptake proteins were identified, modeled, and validated using Ramachandran plots. An in-house antifungal library of ~ 15,401 compounds was screened in molecular docking studies with these proteins. The best small molecule-protein complexes were simulated at 100 ns using Maestro, Schrodinger. Toxicity predictions suggested all six molecules, identified as the best binding compounds to seven proteins, belonged to lower toxicity levels per GHS classification. A molecular mechanics GBSA study for all seven complexes indicated low standard deviations after calculating free binding energies every 10 ns of the 100 ns trajectory. Density functional theory via quantum mechanics approaches highlighted the HOMO, LUMO, and other properties of the six best-bound molecules, revealing their binding capabilities and behaviour. This study sheds light on the molecular mechanisms and protein-ligand interactions, providing a multi-dimensional view towards the use of FDBD01920, FDBD01923, and FDBD01848 as stable antifungal ligands. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00264-7.

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS.

Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose. In this protocol paper, we explore the advantages of using coarse-grained simulations in the study of biomolecular systems, focusing specifically on simulations performed using GROMACS. We discuss the force fields required for these simulations and the types of research questions that can be addressed using coarse-grained simulations. We also highlight the potential benefits of coarse-grained simulations for the development of new force fields and simulation methodologies. We then discuss the expected results from coarse-grained simulations using GROMACS and the various techniques that can be used to analyze these results. We explore the use of trajectory analysis tools, as well as thermodynamic and structural analysis techniques, to gain insight into the behavior of biomolecular systems.

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Carboxymuconolactone decarboxylase is a prospective molecular target for multi-drug resistant Acinetobacter baumannii-computational modeling, molecular docking and dynamic simulation studies.

Multidrug-resistant Acinetobacter baumannii (MDRAb), a priority-I pathogen declared by the World Health Organization, became a potential healthcare concern worldwide with a high mortality rate. Thus, the identification of putative molecular targets and potential lead molecules is an important concern in healthcare. The present study aimed to screen a prospective molecular target and effectual binders for the drug discovery of MDRAb by computational virtual screening approach. Based on the functional role, γ-carboxymuconolactone decarboxylase (CMD) was prioritized as the target and its three-dimensional (3D) structure was computationally modeled. Based on the availability of the 3D structure, twenty-five herbal molecules were selected by database search, and their drug-likeliness, pharmacokinetic, and toxicity features were predicted. The effectual binding of the selected molecules towards CMD was predicted by molecular docking. The stability of the best-docked complexes was predicted by molecular dynamics (MD) simulation for 100 ns and binding energy calculations were carried out by molecular mechanics generalized Born and surface area solvation (MM/GBSA) method. Out of twenty-five molecules screened, hirsutine (an indole alkaloid of Uncaria rhynchophylla) and thymoquinone (a phytochemical of Nigella sativa) were qualified for drug likeliness, pharmacokinetic, and toxicity features and demonstrated significant effectual binding to CMD when compared with the binding of co-crystallized inhibitor and CMD (control). The docked complexes of hirsutine and thymoquinone, and CMD were stabilized by the binding energies of -8. 30 and -8. 46 kcal/mol respectively. These molecules were qualified in terms of ideal drug likeliness, ADME, and toxicity properties. MD simulation studies showed that the ligand-protein complexes were stable throughout the simulation. The binding free energies of the complexes by MMGBSA were estimated to be -42.08157745 kcal/mol and -36.58618242 kcal/mol for hirsutine and thymoquinone respectively when compared with the calculated binding free energy of the control (-28.75032666 kcal/mol). This study concluded that hirsutine and thymoquinone can act as potential lead molecules against CMD and the present hypothesis can be scaled up to develop potential inhibitors against MDRAb.