Molecular insights of protein contour recognition with ligand pharmacophoric sites through combinatorial library design and MD simulation in validating HTLV-1 PR inhibitors

Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer's randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, - 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein-ligand complexes, yielding five promising novel UPPS inhibitors.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its subsidiaries. Egl nine homolog 1 (EGLN1) inhibition prevents poly ubiquitination-mediated proteosomal destruction HIF-1α. The pharmacological interventions aimed at stabilizing HIF-1α have the potential to be a promising treatment option for a range of human diseases, including ischemic stroke. To unveil a novel EGLN1 inhibitor from marine natural products, a custom-based virtual screening was carried out using a Deep Convolutional Neural Network (DCNN) architecture, docking, and molecular dynamics simulation. The custom DCNN model was optimized and further employed to screen marine natural products from the CMNPD database. The docking was performed as a secondary strategy for screened hits. Molecular dynamics (MD) and molecular mechanics/generalized Born surface area (MM-GBSA) were used to analyze inhibitor binding and identify key interactions. The findings support the claim that deep learning-based virtual screening is a rapid, reliable and accurate method of identifying highly contributing drug candidates (EGLN1 inhibitors). This study demonstrates that deep learning architecture can significantly accelerate drug discovery and development, and provides a solid foundation for using (Z)-2-ethylhex-2-enedioic acid [(Z)-2-ethylhex-2-enedioic acid] as a potential EGLN1 inhibitor for treating various health complications.Communicated by Ramaswamy H. Sarma.

Impact of Semiochemicals Binding to Fel d 1 on Its 3D Conformation and Predicted B-Cell Epitopes Using Computational Approaches

The major cat allergen Fel d 1 is a tetrameric glycoprotein from the secretoglobin superfamily. Fel d 1's biological role is unknown, but it has been previously shown that it participates in semiochemical binding/transportation. Fel d 1 has linear epitopes, but its conformational epitope sites remain unclear. In this study, we predicted the B-cell epitopes of Fel d 1 and explored semiochemical dynamics with epitopes using bioinformatics tools. The epitope residues were tabulated for chains 1 and 2 and the heterodimers of Fel d 1. The residual interactions of Fel d 1 with IgE were evaluated, and the prominent epitope sites were predicted. The molecular dynamics simulation (MDS) of Fel d 1 was performed with seven reported semiochemicals to evaluate the Fel d 1-ligand complex stability and decipher the semiochemical effect on Fel d 1 conformational epitopes. Fel d 1-lauric acid, Fel d 1-oleic acid, and Fel d 1-progesterone showed more stability and less fluctuation than other compounds. Fel d 1-linoleic acid and Fel d 1-pregnenolone displayed the most unstable complex with fluctuations. The effects of conformational changes on epitopes are discussed. All the ligand complexes drive substantial fluctuation towards the functionally exposed IgE-binding epitopes. Fel d 1 could be examined for its ligand-binding and conformational changes caused by mutations of B-cell epitopes.

In silico and In vitro Analysis of Nigella sativa Bioactives Against Chorismate Synthase of Listeria monocytogenes: a Target Protein for Biofilm Inhibition

Listeria monocytogenes have the ability to form biofilms, which aid in the contamination of food and the evasion of antimicrobials. Consumption of L. monocytogenes laden food can promote mild to severe infection in humans and cause serious health issues. Therefore, biofilm development by L. monocytogenes is considered to be a major concern for both healthcare and food safety. This study attempted to target chorismate synthase, an essential protein predicted to be involved in the biofilm pathway. Nigella sativa is renowned for its applications in folk medicine; hence, bioactive ingredients reported were used for molecular docking studies. In the absence of a three-dimensional structure of chorismate synthase from L. monocytogenes, a homology model was generated using the Modeller program. A model with the highest DOPE score was chosen and validated. The reliable model was subjected to docking studies with 30 ligands from N. sativa. From this approach, α-longipinene was unveiled as the best hit. Further in vitro studies demonstrated the antibiofilm potential of α-longipinene against L. monocytogenes. Overall, the study reveals lead molecules from N. sativa as promising antibiofilm agents against L. monocytogenes. Hence, extended investigation with lead molecules will provide sustainable strategies to prevent biofilm-mediated problems due to L. monocytogenes.

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Evaluating the structural and immune mechanism of Interleukin-6 for the investigation of goat milk peptides as potential treatments for COVID-19

The function of Immune control, haematopoiesis, and inflammation all depend on the cytokine Interleukin 6 (IL-6), and higher expression of IL-6 is seen in COVID-19 and other diseases. The immune protein IL-6 activation is dependent on binding interactions with IL-6Rα, mIL-6R, and sIL-6R for its cellular function. Termination of these reaction could benefit for controlling the over-expression in COVID-19 patients and that may arise as inhibitors for controlling COVID-19. Traditionally, the goat milk has been prescribed as medicine in ayurvedic practice and through this work, we have explored the benefits of peptides from goat milk as IL-6 inhibitors, and it have the potential of inhibiting the over expression of IL-6 and control the COVID-19 disease. Computational experiments have shown that goat peptides had strong interactions with IL-6, with higher scoring profiles and energy efficiency ranging from −6.00 kcal/mol to −9.00 kcal/mol in docking score and −39.00 kcal/mol in binding energy. Especially the YLGYLEQLLR, VLVLDTDYK and AMKPWIQPK peptides from goat milk holds better scoring and shows strong interactions were identified as the most potential IL-6 inhibitor candidates in this study. Peptides from Goat proteins, which are capable of binding to the IL-6 receptor with strong binding conformations, have no negative effects on other immune system proteins.

Structure and chemistry of enzymatic active sites that play a role in the switch and conformation mechanism

Enzymes, which are biological molecules, are constructed from polypeptide chains, and these molecules are activated through reaction mechanisms. It is the role of enzymes to speed up chemical reactions that are used to build or break down cell structures. Activation energy is reduced by the enzymes' selective binding of substrates in a protected environment. In enzyme tertiary structures, the active sites are commonly situated in a "cleft," which necessitates the diffusion of substrates and products. The amino acid residues of the active site may be far apart in the primary structure owing to the folding required for tertiary structure. Due to their critical role in substrate binding and attraction, changes in amino acid structure at or near the enzyme's active site usually alter enzyme activity. At the enzyme's active site, or where the chemical reactions occur, the substrate is bound. Enzyme substrates are the primary targets of the enzyme's active site, which is designed to assist in the chemical reaction. This chapter elucidates the summary of structure and chemistry of enzymes, their active site features, charges and role of water in the structures to clarify the biochemistry of the enzymes in the depth of atomic features.

An Insight of RuBisCO Evolution through a Multilevel Approach

RuBisCO is the most abundant enzyme on earth; it regulates the organic carbon cycle in the biosphere. Studying its structural evolution will help to develop new strategies of genetic improvement in order to increase food production and mitigate CO₂ emissions. In the present work, we evaluate how the evolution of sequence and structure among isoforms I, II and III of RuBisCO defines their intrinsic flexibility and residue-residue interactions. To do this, we used a multilevel approach based on phylogenetic inferences, multiple sequence alignment, normal mode analysis, and molecular dynamics. Our results show that the three isoforms exhibit greater fluctuation in the loop between αB and βC, and also present a positive correlation with loop 6, an important region for enzymatic activity because it regulates RuBisCO conformational states. Likewise, an increase in the flexibility of the loop structure between αB and βC, as well as Lys330 (form II) and Lys322 (form III) of loop 6, is important to increase photosynthetic efficiency. Thus, the cross-correlation dynamics analysis showed changes in the direction of movement of the secondary structures in the three isoforms. Finally, key amino acid residues related to the flexibility of the RuBisCO structure were indicated, providing important information for its enzymatic engineering.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Structural Understanding of SARS-CoV-2 Drug Targets, Active Site Contour Map Analysis and COVID-19 Therapeutics

The most iconic word of the year 2020 is 'COVID-19', the shortened name for coronavirus disease 2019. The pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is responsible for multiple worldwide lockdowns, an economic crisis, and a substantial increase in hospitalizations for viral pneumonia along with respiratory failure and multiorgan dysfunctions. Recently, the first few vaccines were approved by World Health Organization (WHO) and can eventually save millions of lives. Even though, few emergency use drugs like Remdesivir and several other repurposed drugs, still there is no approved drug for COVID-19. The coronaviral encoded proteins involved in host-cell entry, replication, and host-cell invading mechanism are potentially therapeutic targets. This perspective review provides the molecular overview of SARS-CoV-2 life cycle for summarizing potential drug targets, structural insights, active site contour map analyses of those selected SARS-CoV-2 protein targets for drug discovery, immunology, and pathogenesis.

Interrogation of Bacillus anthracis SrtA active site loop forming open/close lid conformations through extensive MD simulations for understanding binding selectivity of SrtA inhibitors

Bacillus anthracis is a gram positive, deadly spore forming bacteria causing anthrax and these bacteria having the complex mechanism in the cell wall envelope, which can adopt the changes in environmental conditions. In this, the membrane bound cell wall proteins are said to progressive drug target for the inhibition of Bacillus anthracis. Among the cell wall proteins, the SrtA is one of the important mechanistic protein, which mediate the ligation with LPXTG motif by forming the amide bonds. The SrtA plays the vital role in cell signalling, cell wall formation, and biofilm formations. Inhibition of SrtA leads to rupture of the cell wall and biofilm formation, and that leads to inhibition of Bacillus anthracis and thus, SrtA is core important enzyme to study the inhibition mechanism. In this study, we have examined 28 compounds, which have the inhibitory activity against the Bacillus anthracis SrtA for developing the 3D-QSAR and also, compounds binding selectivity with both open and closed SrtA conformations, obtained from 100 ns of MD simulations. The binding site loop deviate in forming the open and closed gate mechanism is investigated to understand the inhibitory profile of reported compounds, and results show the closed state active site conformations are required for ligand binding specificity. Overall, the present study may offer an opportunity for better understanding of the mechanism of action and can be aided to further designing of a novel and highly potent SrtA inhibitors.

Structure insights of SARS-CoV-2 open state envelope protein and inhibiting through active phytochemical of ayurvedic medicinal plants from Withania somnifera

Coronaviruses have been causing pandemic situations across the globe for the past two decades and the focus is on identifying suitable novel targets for antivirals and vaccine development. SARS-CoV-2 encodes a small hydrophobic envelope (E) protein that mediates envelope formation, budding, replication, and release of progeny viruses from the host. Through this study, the SARS-CoV-2 E protein is studied for its open and closed state and focused in identifying antiviral herbs used in traditional medicine practices for COVID-19 infections. In this study using computational tools, we docked the shortlisted phytochemicals with the envelope protein of the SARS-CoV-2 virus and the results hint that these compounds interact with the pore-lining residues. The molecular level understanding of the open state is considered and the active inhibitors from the phytochemicals of Ayurvedic medicinal plants from Withania somnifera. We have thus identified a potential phytochemical compound that directly binds with the pore region of the E protein and thereby blocks its channel activity. Blocking the ion channel activity of E protein is directly related to the inhibition of virus replication. The study shows encouraging results on the usage of these phytochemicals in the treatment/management of SARS-CoV-2 infection.

Pharmacophore based virtual screening, molecular docking and molecular dynamic simulation studies for finding ROS1 kinase inhibitors as potential drug molecules

Proto-oncogene receptor tyrosine kinase ROS-1 is one of the clinically important biomarker and plays a crucial role in regulation of a number of cellular functions including cell proliferation, migration and angiogenesis. Recently, inhibition of ROS1 kinase has proven to be a promising target of anticancer drugs for non-small cell lung cancer (NSCLC). The very few compounds have been used as potent drug molecules so far and the selective ROS1 inhibitors are relatively rare. Besides the currently available drugs such as Crizotinib and PF-06463922 are becoming sensitive due to mutations in the ROS1 protein. To curtail the problem of the resistant, present study was designed to identify the potent inhibitors against ROS1. Three different screening approaches such as structure based, Atom-based and pharmacophore based screening were carried out against commercially available databases and the retrieved best hits were further evaluated by Lipinski's filter. Thereafter the lead molecule was subjected to pocket specific docking with ROS1. The results show that, total of 9 molecules (3 from each screening) has good docking score (with range of -9.288 to -12.49 Kcal/Mol) and binding interactions within the active site of ROS1. In order to analyze the stability of the ligand- protein complexes, molecular dynamics simulation was performed. Thus, these identified potential lead molecules with good binding score and binding affinity with ROS1 may act as the potent ROS1 inhibitor, and that are worth considering for further experimental studies.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 (COVID-19), researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5′-ends of viral genomic RNA and sub genomic RNAs, to escape the host’s innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5’,5’-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5’-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.

Communicated by Ramaswamy H. Sarma.

Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7

High risk human papillomaviruses are highly associated with the cervical carcinoma and the other genital tumors. Development of cervical cancer passes through the multistep process initiated from benign cyst to increasingly severe premalignant dysplastic lesions in an epithelium. Replication of this virus occurs in the fatal differentiating epithelium and involves in the activation of cellular DNA replication proteins. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers constrains the cells into S-phase constructing an environment favorable for genome replication and cell proliferation. To date, no suitable drug molecules exist to treat HPV infection whereas anticipation of novel anti-HPV chemotherapies with distinctive mode of actions and identification of potential drugs are crucial to a greater extent. Hence, our present study focused on identification of compounds analogue to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems towards treatment of cancer. A three dimensional similarity search on the small molecule library from natural product database using EGCG identified 11 potential small molecules based on their structural similarity. The docking strategies were implemented with acquired small molecules and identification of the key interactions between protein and compounds were carried out through binding free energy calculations. The conformational changes between the apoprotein and complexes were analyzed through simulation performed thrice demonstrating the dynamical and structural effects of the protein induced by the compounds signifying the domination. The analysis of the conformational stability provoked us to describe the features of the best identified small molecules through electronic structure calculations. Overall, our study provides the basis for structural insights of the identified potential identified small molecules and EGCG. Hence, the identified analogue of EGCG can be potent inhibitors against the HPV 16 E7 oncoprotein.

Exploring the effect of inhibitor AKB-9778 on VE-PTP by molecular docking and molecular dynamics simulation

Diabetic macular edema, also known as diabetic eye disease, is mainly caused by the overexpression of vascular endothelial protein tyrosine phosphatase (VE-PTP) at hypoxia/ischemic. AKB-9778 is a known VE-PTP inhibitor that can effectively interact with the active site of VE-PTP to inhibit the activity of VE-PTP. However, the binding pattern of VE-PTP with AKB-9778 and the dynamic implications of AKB-9778 on VE-PTP system at the molecular level are poorly understood. Through molecular docking, it was found that the AKB-9778 was docked well in the binding pocket of VE-PTP by the interactions of hydrogen bond and Van der Waals. Furthermore, after molecular dynamic simulations on VE-PTP system and VE-PTP ^AKB-9778 system, a series of postdynamic analyses found that the flexibility and conformation of the active site undergone an obvious transition after VE-PTP binding with AKB-9778. Moreover, by constructing the RIN, it was found that the different interactions in the active site were the detailed reasons for the conformational differences between these two systems. Thus, the finding here might provide a deeper understanding of AKB-9778 as VE-PTP Inhibitor.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

Structural insights into the binding mode of flavonols with the active site of matrix metalloproteinase-9 through molecular docking and molecular dynamic simulations studies

Cartilage degradation in rheumatoid arthritis is mediated principally by the collagenases and gelatinases. Gelatinase B (also called matrix metalloproteinase 9 - MMP-9), is a valid target molecule which is known to participate in cartilage degradation as well as angiogenesis associated with the disease and inhibition of its activity shall prevent cartilage damage and angiogenesis. The focus of this study is to investigate the possibilities of MMP-9 inhibition by flavonol class of bioflavonoids by studying their crucial binding interactions at the active site of MMP 9 using molecular docking (Glide XP and QPLD) and further improvisation by post-docking MM-GBSA and molecular dynamic (MD) simulations. The results show that flavonols can convincingly bind to active site of MMP-9 as demonstrated by their stable interactions at the S1' specificity pocket and favourable binding energies. Gossypin has emerged as a promising candidate with a docking score of -14.618 kcal/mol, binding energy of -79.97 kcal/mol and a stable MD pattern over 15 ns. In addition, interaction mechanisms with respect to catalytic site zinc are also discussed. Further, the drug-like characters of the ligands were also analysed using ADME analysis.

A highly efficient sorbitol dehydrogenase from Gluconobacter oxydans G624 and improvement of its stability through immobilization

A sorbitol dehydrogenase (GoSLDH) from Gluconobacter oxydans G624 (G. oxydans G624) was expressed in Escherichia coli BL21(DE3)-CodonPlus RIL. The complete 1455-bp codon-optimized gene was amplified, expressed, and thoroughly characterized for the first time. GoSLDH exhibited K_m and k_cat values of 38.9 mM and 3820 s⁻¹ toward L-sorbitol, respectively. The enzyme exhibited high preference for NADP⁺ (vs. only 2.5% relative activity with NAD⁺). GoSLDH sequencing, structure analyses, and biochemical studies, suggested that it belongs to the NADP⁺-dependent polyol-specific long-chain sorbitol dehydrogenase family. GoSLDH is the first fully characterized SLDH to date, and it is distinguished from other L-sorbose-producing enzymes by its high activity and substrate specificity. Isothermal titration calorimetry showed that the protein binds more strongly to D-sorbitol than other L-sorbose-producing enzymes, and substrate docking analysis confirmed a higher turnover rate. The high oxidation potential of GoSLDH for D-sorbitol was confirmed by cyclovoltametric analysis. Further, stability of GoSLDH significantly improved (up to 13.6-fold) after cross-linking of immobilized enzyme on silica nanoparticles and retained 62.8% residual activity after 10 cycles of reuse. Therefore, immobilized GoSLDH may be useful for L-sorbose production from D-sorbitol.

Mechanistic insights of SrtA–LPXTG blockers targeting the transpeptidase mechanism in Streptococcus mutans

The SrtA–LPXTG interaction plays a key role in transpeptidation reaction, cell wall and biofilm formations. This study explains the blocking of LEU interactions with SrtA will results as SrtA inhibitors through MD simulation and energy calculations methods.