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.

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.

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.

BRCA1/TP53 tumor proteins inhibited by novel analogues of curcumin - Insight from computational modelling, dynamic simulation and experimental validation.

The current study aimed to design novel curcumin analogue inhibitors with antiproliferative and antitumor activity towards BRCA1 and TP53 tumor proteins and to study their therapeutic potential by computer-aided molecular designing and experimental investigations. Four curcumin analogues were computationally designed and their drug-likeness and pharmacokinetic properties were predicted. The binding of these analogues against six protein targets belonging to BRCA1 and TP53 tumor proteins were modelled by molecular docking and their binding energies were compared with that of curcumin and the standard drug cyclophosphamide and its validated target. The stabilities of selected docked complexes were confirmed by molecular dynamic simulation (MDS) and MMGBSA calculations. The best-docked analogue was chemically synthesized, characterized, and used for in vitro cytotoxic screening using DLA, EAC, and C127I cell lines. In vivo antitumor studies were carried out in Swiss Albino Mice. The study revealed that the designed analogues satisfied drug-likeness and pharmacokinetic properties and demonstrated better binding affinity to the selected targets than curcumin. Among the analogues, NLH demonstrated significant interaction with the BRCA1-BRCT-c domain (TG3; binding energy -8.3 kcal/mol) when compared to the interaction of curcumin (binding energy -6.19 kcal) and cyclophosphamide (binding energy -3.8 kcal/mol) and its usual substrate (TG7). The MDS and MM/GBSA studies revealed that the binding free energy of the NLH-TG3 complex (-61.24 kcal/mol) was better when compared to that of the cyclophosphamide-TG7 complex (-21.67 kcal/mol). In vitro, cytotoxic studies showed that NLH demonstrated significant antiproliferative activities against tumor cell lines. The in vivo study depicted NLH possesses the potential for tumor inhibition. Thus, the newly synthesized curcumin analogue is probably used to develop novel therapeutic agents against breast cancer.

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.

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.

Design of Novel Imidazopyrazine Derivative against Breast Cancer via Targeted NPY1R Antagonist.

INTRODUCTION: Breast cancer is the most frequent malignancy in women with more than one in ten new cancer diagnoses each year. Synthetic products are a key source for the identification of new anticancer medicines and drug leads. OBJECTIVES: Imidazopyrazine is a highly favored skeleton for the design of new anticancer drugs. In silico designed derivatives were screened using computer aided drug design techniques and validated using MTT assay. METHODS: A template-based methodology was used in the current work to create novel Imidazopyrazine derivatives, targeting the NPY1R protein. Molecular docking, Diffusion docking, MD simulation, MM-GBSA and meta-dynamics techniques were followed. MTT assay was performed to validate the activity of principal compound. RESULTS: A docking score of -6.660 and MMGBSA value of -108.008 (+/-) 9.14 kcal/mol was obtained from the investigations conducted. In addition, molecular dynamics simulation was carried out for 500 ns, yielding a stable RMSD and value of 5.6 Å, thus providing insights on the stability of the protein conformation on interaction with the principal compound. Furthermore, the in vivo validation studies conducted via MTT assay showed an IC50 value of 73.45 (+/-) 0.45 µg /mL. CONCLUSION: The research has produced encouraging findings and can be applied as a model for precise enumerations in the future. It also encourages the study of novel synthetic compounds with potential anti-cancer properties.

Insights on Novel Effectors and Characterization of Metacaspase (RS107_6) as a Potential Cell Death-Inducing Protein in Rhizoctonia solani.

Effectors play an important role in host-pathogen interactions. Though an economically significant disease in rice, knowledge regarding the infection strategy of Rhizoctonia solani is obscure. In this study, we performed a genome-wide identification of the effectors in R. solani based on the characteristics of previously reported effector proteins. A total of seven novel effectors (designated as RS107_1 to RS107_7) in the disease mechanism of R. solani were identified and were predicted to be non-classically secreted proteins with functionally conserved domains. The function, reactivity, and stability of these proteins were evaluated through physiochemical characterization. The target proteins involved in the regulation of rice defense mechanisms were identified. Furthermore, the effector genes were cloned and RS107_6 (metacaspase) was heterologously expressed in Escherichia coli to obtain a purified protein of ~36.5 kDa. The MALD-TOF characterization confirmed that the protein belonged to a metacaspase of the Peptidase_C14 protein family, 906 bp in size, and encoded a polypeptide of 301 amino acids. These findings suggest that the identified effectors can potentially serve as a virulence factor and can be targeted for the management of sheath blight in rice.

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.

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.

Coumarin derivative as a potent drug candidate against triple negative breast cancer targeting the frizzled receptor of wingless-related integration site signaling pathway.

Triple negative breast cancer constitutes to about 21.8 percent of the total breast cancer related cases. Its ability to affect young ladies and in pre-menstrual stage makes this a disease of concern worldwide. The current treatment regimens involve chemotherapy which are used for treatment of other cancer types. In this regard, there is a need for specific and targeted drug candidate for its effective treatment. In the current study, assessment of coumarin derivative 2-(2-(6- Methyl-2-Oxo-2H-chromen-4-yl) acetamido)-3-phenylpropanoic acid is carried out both In-silico and In-vitro methods. Frizzled transmembrane proteins of Wingless-related integration site signaling pathway was targeted in which Frizzled-7 proved to a prospective target and showed a binding energy of -6.78 kcal/mol. The complex was subjected to molecular dynamics simulation for 200 ns and showed stable interaction with cysteine rich domain of the receptor. Cell proliferation, viability and apoptosis assay were performed on MDA-MB-231 and MDA-MB-468 cell lines with an IC50 value of 81.23 and 84.68 µM, respectively. The results provide a drug candidate which is derivative of a natural compound with targeted TNBC inhibitory effect. Communicated by Ramaswamy H. Sarma.

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.

Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid.

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-κB target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-κB that can be useful in drug repurposing in COVID-19 therapy.

Novel 1,2,5-Trisubstituted Benzimidazoles Potentiate Apoptosis by Mitochondrial Dysfunction in Panel of Cancer Cells.

Synthetic small molecules have been very effective in decimating cancer cells by targeting various aberrantly overexpressed oncogenic proteins. These small molecules target proteins involved in cell cycle regulation, cell division, migration, invasion, angiogenesis, and other regulatory proteins to induce apoptosis in cancer cells. In this study, we have synthesized a novel 1,2,5-trisubstituted benzimidazole chemical library of small molecules and unveiled their anticancer potential against a panel of cancer cell lines such as Jurkat, K-562, MOLT-4, HeLa, HCT116, and MIA PaCa-2 cancer cells. The MTT assay and Trypan blue dye exclusion assay clearly unveiled the cytotoxic effect of methyl 1-benzyl-2-(4-fluoro-3-nitrophenyl)-1H-benzo[d]imidazole-5-carboxylate (TJ08) and its potential to induce apoptosis with effective IC50 of 1.88 ± 0.51, 1.89 ± 0.55, 2.05 ± 0.72, 2.11 ± 0.62, 3.04 ± 0.8, and 3.82 ± 0.25 µM against Jurkat, K562, MOLT-4, HeLa, HCT116, and MIA PaCa-2 cancer cell lines, respectively. Altered mitochondrial membrane potential was observed in HeLa, HCT116, and Jurkat cells due to TJ08 treatment, which was unveiled by JC10 staining. Induction of early and late apoptosis by TJ08 treatment was also unveiled by apoptotic analysis and immunofluorescence imaging. Cell cycle analysis distribution confirms the accumulation of cells in the S-phase in a dose-dependent manner.

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.

Correction to: Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.

[This corrects the article DOI: 10.1007/s11224-022-02020-z.].

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2.

The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

Novel small molecules targeting bZIP23 TF improve stomatal conductance and photosynthesis under mild drought stress by regulating ABA.

Drought-induced abscisic acid (ABA) accumulation plays a key role in plant water relations by regulating stomatal movements. Although ABA helps in the survival of the plants, reduced carbon gain affects plant productivity. To improve crop productivity under mild drought stress conditions, it is necessary to manipulate ABA responses. Other research groups have used forward chemical genomics for the identification of ABA agonists and antagonists aiming to manipulate ABA biosynthesis and signalling. In the present study, we identified indolyl-ethyl amine and serotonin small molecules using a reverse chemical genomics approach, with these acting as potent inhibitors of ABA biosynthesis through transient regulation of bZIP23 transcription factor activity. In rice, wheat and soybean, each of the small molecules enhanced the germination of seeds, even in the presence of ABA. These molecules nullified the effect of ABA on intact and detached leaves, resulting in higher photosynthesis. Furthermore, these small molecules effectively reduced the transcription levels of bZIP23 targeting NCED4, PP2C49 and CO3 genes. Rice plants treated with the small molecules were found to have improved stomatal conductance, spikelet fertility and yield compared to untreated plants under mild drought stress conditions. Our results suggest that indolyl-ethyl amine and serotonin small molecules could be utilized to improve yield under mild drought conditions.

Understanding the Xylooligosaccharides Utilization Mechanism of Lactobacillus brevis and Bifidobacterium adolescentis: Proteins Involved and Their Conformational Stabilities for Effectual Binding.

Xylooligosaccharides having various degrees of polymerization such as xylobiose, xylotriose, and xylotetraose positively affect human health by interacting with gut proteins. The present study aimed to identify proteins present in gut microflora, such as xylosidase, xylulokinase, etc., with the help of retrieved whole-genome annotations and find out the mechanistic interactions of those with the above substrates. The 3D structures of proteins, namely Endo-1,4-beta-xylanase B (XynB) from Lactobacillus brevis and beta-D-xylosidase (Xyl3) from Bifidobacterium adolescentis, were computationally predicted and validated with the help of various bioinformatics tools. Molecular docking studies identified the effectual binding of these proteins to the xylooligosaccharides, and the stabilities of the best-docked complexes were analyzed by molecular dynamic simulation. The present study demonstrated that XynB and Xyl3 showed better effectual binding toward Xylobiose with the binding energies of - 5.96 kcal/mol and - 4.2 kcal/mol, respectively. The interactions were stabilized by several hydrogen bonding having desolvation energy (- 6.59 and - 7.91). The conformational stabilities of the docked complexes were observed in the four selected complexes of XynB-xylotriose, XynB-xylotetraose, Xyl3-xylobiose, and Xyn3-xylotriose by MD simulations. This study showed that the interactions of these four complexes are stable, which means they have complex metabolic activities among each other. Extending these studies of understanding, the interaction between specific probiotics enzymes and their ligands can explore the detailed design of synbiotics in the future.