Molecular docking and dynamic approach to screen the drug candidate against the Imipenem-resistant CarO porin in Acinetobacter baumannii.

The multidrug-resistant Acinetobacter baumannii is an emerging nosocomial pathogen in the healthcare sector. Intrinsic resistance in A. baumannii is a significant problem framing a perfect treatment regimen. Also, this organism showed more resistance towards the carbapenem antibiotics, especially for imipenem and meropenem. The development of carbapenem-resistant Acinetobacter baumannii is mainly due to the alteration or loss of the porin region in the outer membrane. The most well-known porin in Acinetobacter baumannii is CarO (carbapenem-associated outer membrane protein). The CarO protein, which functions as a porin channel for carbapenem inflow, may contribute to carbapenem resistance. The current study identifies a potent drug candidate with a better binding affinity to the carbapenem-resistant outer membrane protein. We investigated the specificity of carbapenems such as imipenem, meropenem, ertapenem, biapenem, doripenem, and fluoroquinolone drugs such as sitafloxacin against the imipenem-resistant CarO protein was demonstrated using the computational approaches molecular docking and dynamic simulation for 50 ns. As a result, the high to low enzyme-ligand complex's binding affinity exhibited a greater binding affinity for ertapenem -7.76 kcal·mol-1 and sitafloxacin -7.75 kcal·mol-1 than biapenem, doripenem, meropenem, and imipenem. The molecular dynamic simulation and the MMPBSA analysis depicted ertapenem -55.431±25.908 kJ/mol and sitafloxacin -47.154 ± 11.052 kJ/mol with better binding affinity and more stability against the imipenem resistant CarO protein when it compared to other antibiotics.

Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridiumdifficile.

Antimicrobial resistance has caused chaos worldwide due to the depiction of multidrug-resistant (MDR) infective microorganisms. A thorough examination of antimicrobial resistance (AMR) genes and associated resistant mechanisms is vital to solving this problem. Clostridium difficile (C. difficile) is an opportunistic nosocomial bacterial strain that has acquired exogenous AMR genes that confer resistance to antimicrobials such as erythromycin, azithromycin, clarithromycin, rifampicin, moxifloxacin, fluoroquinolones, vancomycin, and others. A network of interactions, including 20 AMR genes, was created and analyzed. In functional enrichment analysis, Cellular components (CC), Molecular Functions (MF), and Biological Processes (BP) were discovered to have substantial involvement. Mutations in the rpl genes, which encode ribosomal proteins, confer resistance in Gram-positive bacteria. Full erythromycin and azithromycin cross-resistance can be conferred if more than one of the abovementioned genes is present. In the enriched BP, rps genes related to transcriptional regulation and biosynthesis were found. The genes belong to the rpoB gene family, which has previously been related to rifampicin resistance. The genes rpoB, gyrA, gyrB, rpoS, rpl genes, rps genes, and Van genes are thought to be the hub genes implicated in resistance in C. difficile. As a result, new medications could be developed using these genes. Overall, our observations provide a thorough understanding of C. difficile AMR mechanisms.

Phage-based therapy against biofilm producers in gram-negative ESKAPE pathogens.

Persistent antibiotic use results in the rise of antimicrobial resistance with limited or no choice for multidrug-resistant (MDR) and extensively drug resistant (XDR) bacteria. This necessitates a need for alternative therapy to effectively combat clinical pathogens that are resistant to last resort antibiotics. The study investigates hospital sewage as a potential source of bacteriophages to control resistant bacterial pathogens. Eighty-one samples were screened for phages against selected clinical pathogens. Totally, 10 phages were isolated against A. baumannii, 5 phages against K. pneumoniae, and 16 phages were obtained against P. aeruginosa. The novel phages were observed to be strain-specific with complete bacterial growth inhibition of up to 6 h as monotherapy without antibiotics. Phage plus colistin combinations reduced the minimum-biofilm eradication concentration of colistin up to 16 folds. Notably, a cocktail of phages exhibited maximum efficacy with complete killing at 0.5-1 µg/ml colistin concentrations. Thus, phages specific to clinical strains have a higher edge in treating nosocomial pathogens with their proven anti-biofilm efficacy. In addition, analysis of phage genomes revealed close phylogenetic relations with phages reported from Europe, China, and other neighbouring countries. This study serves as a reference and can be extended to other antibiotics and phage types to assess optimum synergistic combinations to combat various drug resistant pathogens in the ongoing AMR crisis.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach.

The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics. This study aimed to use machine-learning techniques of expressed genes in patients with AD to identify potential biomarkers that can be used for future therapy. The dataset is accessed from the Gene Expression Omnibus (GEO) database (Accession Number: GSE36980). The subgroups (AD blood samples from frontal, hippocampal, and temporal regions) are individually investigated against non-AD models. Prioritized gene cluster analyses are conducted with the STRING database. The candidate gene biomarkers were trained with various supervised machine-learning (ML) classification algorithms. The interpretation of the model prediction is perpetrated with explainable artificial intelligence (AI) techniques. This experiment revealed 34, 60, and 28 genes as target biomarkers of AD mapped from the frontal, hippocampal, and temporal regions. It is identified ORAI2 as a shared biomarker in all three areas strongly associated with AD's progression. The pathway analysis showed that STIM1 and TRPC3 are strongly associated with ORAI2. We found three hub genes, TPI1, STIM1, and TRPC3, in the network of the ORAI2 gene that might be involved in the molecular pathogenesis of AD. Naive Bayes classified the samples of different groups by fivefold cross-validation with 100% accuracy. AI and ML are promising tools in identifying disease-associated genes that will advance the field of targeted therapeutics against genetic diseases.

Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease.

Alzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11 years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP + + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD.

Fruit ripening: dynamics and integrated analysis of carotenoids and anthocyanins.

BACKGROUND: Fruits are vital food resources as they are loaded with bioactive compounds varying with different stages of ripening. As the fruit ripens, a dynamic color change is observed from green to yellow to red due to the biosynthesis of pigments like chlorophyll, carotenoids, and anthocyanins. Apart from making the fruit attractive and being a visual indicator of the ripening status, pigments add value to a ripened fruit by making them a source of nutraceuticals and industrial products. As the fruit matures, it undergoes biochemical changes which alter the pigment composition of fruits. RESULTS: The synthesis, degradation and retention pathways of fruit pigments are mediated by hormonal, genetic, and environmental factors. Manipulation of the underlying regulatory mechanisms during fruit ripening suggests ways to enhance the desired pigments in fruits by biotechnological interventions. Here we report, in-depth insight into the dynamics of a pigment change in ripening and the regulatory mechanisms in action. CONCLUSIONS: This review emphasizes the role of pigments as an asset to a ripened fruit as they augment the nutritive value, antioxidant levels and the net carbon gain of fruits; pigments are a source for fruit biofortification have tremendous industrial value along with being a tool to predict the harvest. This report will be of great utility to the harvesters, traders, consumers, and natural product divisions to extract the leading nutraceutical and industrial potential of preferred pigments biosynthesized at different fruit ripening stages.

A computational method to characterize the missense mutations in the catalytic domain of GAA protein causing Pompe disease.

Pompe disease is an autosomal recessive lysosomal storage disease caused by acid α-glucosidase (GAA) deficiency, resulting in intralysosomal accumulation of glycogen, including cardiac, skeletal, and smooth muscle cells. The GAA gene is located on chromosome 17 (17q25.3), the GAA protein consists of 952 amino acids; of which 378 amino acids (347-726) falls within the catalytic domain of the protein and comprises of active sites (518 and 521) and binding sites (404, 600, 616, and 674). In this study, we used several computational tools to classify the missense mutations in the catalytic domain of GAA for their pathogenicity and stability. Eight missense mutations (R437C, G478R, N573H, Y575S, G605D, V642D, L705P, and L712P) were predicted to be pathogenic and destabilizing to the protein structure. These mutations were further subjected to phenotyping analysis using SNPeffect 4.0 to predict the chaperone binding sites and structural stability of the protein. The mutations R437C and G478R were found to compromise the chaperone-binding activity with GAA. Molecular docking analysis revealed that the G478R mutation to be more significant and hinders binding to the DNJ (Miglustat) compared with the R437C. Further molecular dynamic analysis for the two mutations demonstrated that the G478R mutation was acquired higher deviation, fluctuation, and lower compactness with decreased intramolecular hydrogen bonds compared to the mutant R437C. These data are expected to serve as a platform for drug design against Pompe disease and will serve as an ultimate tool for variant classification and interpretations.

An Integrated Computational Framework to Assess the Mutational Landscape of α-L-Iduronidase IDUA Gene.

Mucopolysaccharidosis type I is a lysosomal genetic disorder caused due to the deficiency of the α-L-iduronidase enzyme (IDUA). Mutations associated with IDUA lead to mild to severe forms of diseases characterized by different clinical features. In the present study, we first performed a comprehensive analysis using various in silico prediction tools to screen and prioritize the missense mutations or nonsynonymous SNPs (nsSNPs) associated with IDUA. Subsequently, statistical analysis was empowered to examine the predictive ability and accuracy of the in silico prediction tool results supporting the disease phenotype ranging from mild to severe. Till date, no study has been carried out in IDUA in analyzing the impact of the nsSNPs at the structural level. In this context with the aid of pathogenic and stability prediction in silico tools, we identified nsSNPs R89Q, R89W, and P533R to be most deleterious and disease-causing having impact on the function of the protein. Extensive molecular dynamics analysis was performed using Gromacs to understand the deleterious nature of the mutants. Variations observed between the trajectory files of native and mutants R89Q, R89W, and P533R using Gromacs utilities enabled us to measure the adverse effects on the protein and could be the underlying reasons for the disease pathogenesis. These findings may be helpful in understanding the genotype-phenotype relationship and molecular basis of the disease to design drugs for better treatment. J. Cell. Biochem. 119: 555-565, 2018. © 2017 Wiley Periodicals, Inc.

Identification of novel heterozygous Apex 1 gene variant (Glu87Gln) in patients with head and neck cancer of Indian origin.

Gene polymorphism among humans is one of the factors governing individual's susceptibility and resistance to various diseases including cancer. DNA repair enzymes play an important role in protecting our genome from various mutagens and preventing cancer. The role of DNA repair enzyme Apurinic/Apyrimidinic endodeoxyribonuclease 1 (Apex 1) in cancer has been very well documented. Using genomic DNA, Apex 1 coding region of 76 patients (n = 76) with head and neck cancer were amplified and sequenced to detect variations in the sequence. Of 76 patients, 1 patient with heterozygous novel Apex 1 variant (Glu87Gln) was identified. A comparative analysis of wild type and variant protein using in silico approach was performed to understand the difference in the structure and the function. This further revealed that the variant had a slight impact on the structure, which affected the stability and function of the protein. Using the state-of-the-art Molecular dynamic simulation analysis, we observed a loss in number of hydrogen bonds and salt bridge with a substitution of Gln for Glu at Position 87. This could be a possible reason behind the loss of stability/function of the protein. This study revealed a new variant of the Apex 1 gene; further studies will lead to the novel roles played by the variant Apex 1 protein in cause, disease progression, and response to the treatment in patients with cancer with Glu87Gln variant.

A profound computational study to prioritize the disease-causing mutations in PRPS1 gene.

Charcot-Marie-Tooth disease (CMT) is one of the most commonly inherited congenital neurological disorders, affecting approximately 1 in 2500 in the US. About 80 genes were found to be in association with CMT. The phosphoribosyl pyrophosphate synthetase 1 (PRPS1) is an essential enzyme in the primary stage of de novo and salvage nucleotide synthesis. The mutations in the PRPS1 gene leads to X-linked Charcot-Marie-Tooth neuropathy type 5 (CMTX5), PRS super activity, Arts syndrome, X-linked deafness-1, breast cancer, and colorectal cancer. In the present study, we obtained 20 missense mutations from UniProt and dbSNP databases and applied series of comprehensive in silico prediction methods to assess the degree of pathogenicity and stability. In silico tools predicted four missense mutations (D52H, M115 T, L152P, and D203H) to be potential disease causing mutations. We further subjected the four mutations along with native protein to 50 ns molecular dynamics simulation (MDS) using Gromacs package. The resulting trajectory files were analyzed to understand the stability differences caused by the mutations. We used the Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), solvent accessibility surface area (SASA), Covariance matrix, Principal Component Analysis (PCA), Free Energy Landscape (FEL), and secondary structure analysis to assess the structural changes in the protein upon mutation. Our study suggests that the four mutations might affect the PRPS1 protein function and stability of the structure. The proposed study may serve as a platform for drug repositioning and personalized medicine for diseases that are caused by the PRPS1 deficiency.

Computational insights of K1444N substitution in GAP-related domain of NF1 gene associated with neurofibromatosis type 1 disease: a molecular modeling and dynamics approach.

The NF1 gene encodes for neurofibromin protein, which is ubiquitously expressed, but most highly in the central nervous system. Non-synonymous SNPs (nsSNPs) in the NF1 gene were found to be associated with Neurofibromatosis Type 1 disease, which is characterized by the growth of tumors along nerves in the skin, brain, and other parts of the body. In this study, we used several in silico predictions tools to analyze 16 nsSNPs in the RAS-GAP domain of neurofibromin, the K1444N (K1423N) mutation was predicted as the most pathogenic. The comparative molecular dynamic simulation (MDS; 50 ns) between the wild type and the K1444N (K1423N) mutant suggested a significant change in the electrostatic potential. In addition, the RMSD, RMSF, Rg, hydrogen bonds, and PCA analysis confirmed the loss of flexibility and increase in compactness of the mutant protein. Further, SASA analysis revealed exchange between hydrophobic and hydrophilic residues from the core of the RAS-GAP domain to the surface of the mutant domain, consistent with the secondary structure analysis that showed significant alteration in the mutant protein conformation. Our data concludes that the K1444N (K1423N) mutant lead to increasing the rigidity and compactness of the protein. This study provides evidence of the benefits of the computational tools in predicting the pathogenicity of genetic mutations and suggests the application of MDS and different in silico prediction tools for variant assessment and classification in genetic clinics.

Computational approach to unravel the impact of missense mutations of proteins (D2HGDH and IDH2) causing D-2-hydroxyglutaric aciduria 2.

The 2-hydroxyglutaric aciduria (2-HGA) is a rare neurometabolic disorder that leads to the development of brain damage. It is classified into three categories: D-2-HGA, L-2-HGA, and combined D,L-2-HGA. The D-2-HGA includes two subtypes: type I and type II caused by the mutations in D2HGDH and IDH2 proteins, respectively. In this study, we studied six mutations, four in the D2HGDH (I147S, D375Y, N439D, and V444A) and two in the IDH2 proteins (R140G, R140Q). We performed in silico analysis to investigate the pathogenicity and stability changes of the mutant proteins using pathogenicity (PANTHER, PhD-SNP, SIFT, SNAP, and META-SNP) and stability (i-Mutant, MUpro, and iStable) predictors. All the mutations of both D2HGDH and IDH2 proteins were predicted as disease causing except V444A, which was predicted as neutral by SIFT. All the mutants were also predicted to be destabilizing the protein except the mutants D375Y and N439D. DSSP plugin of the PyMOL and Molecular Dynamics Simulations (MDS) were used to study the structural changes in the mutant proteins. In the case of D2HGDH protein, the mutations I147S and V444A that are positioned in the beta sheet region exhibited higher Root Mean Square Deviation (RMSD), decrease in compactness and number of intramolecular hydrogen bonds compared to the mutations N439D and D375Y that are positioned in the turn and loop region, respectively. While the mutants R140Q and R140QG that are positioned in the alpha helix region of the protein. MDS results revealed the mutation R140Q to be more destabilizing (higher RMSD values, decrease in compactness and number of intramolecular hydrogen bonds) compared to the mutation R140G of the IDH2 protein. This study is expected to serve as a platform for drug development against 2-HGA and pave the way for more accurate variant assessment and classification for patients with genetic diseases.

Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson's and Gaucher diseases.

Gaucher's disease (GD) is a genetic disorder in which glucocerebroside accumulates in cells and specific organs. It is broadly classified into type I, type II and type III. Patients with GD are at high risk of Parkinson's disease (PD), and the clinical and pathological presentation of GD patients with PD is almost identical to idiopathic PD. Several experimental models like cell culture, animal models, and transgenic mice models were used to understand the molecular mechanism behind GD and PD association; however, such mechanism remains unclear. In this context, based on literature reports, we identified the most common mutations K198T, E326K, T369M, N370S, V394L, D409H, L444P, and R496H, in the Glucosylceramidase (GBA) protein that are known to cause GD1, and represent a risk of developing PD. However, to date, no computational analyses have designed to elucidate the potential functional role of GD mutations with increased risk of PD. The present computational pipeline allows us to understand the structural and functional significance of these GBA mutations with PD. Based on the published data, the most common and severe mutations were E326K, N370S, and L444P, which further selected for our computational analysis. PredictSNP and iStable servers predicted L444P mutant to be the most deleterious and responsible for the protein destabilization, followed by the N370S mutation. Further, we used the structural analysis and molecular dynamics approach to compare the most frequent deleterious mutations (N370S and L444P) with the mild mutation E326K. The structural analysis demonstrated that the location of E326K and N370S in the alpha helix region of the protein whereas the mutant L444P was in the starting region of the beta sheet, which might explain the predicted pathogenicity level and destabilization effect of the L444P mutant. Finally, Molecular Dynamics (MD) at 50 ns showed the highest deviation and fluctuation pattern in the L444P mutant compared to the two mutants E326K and N370S and the native protein. This was consistent with more loss of intramolecular hydrogen bonds and less compaction of the radius of gyration in the L444P mutant. The proposed study is anticipated to serve as a potential platform to understand the mechanism of the association between GD and PD, and might facilitate the process of drug discovery against both GD and PD.

Impact of missense mutations in survival motor neuron protein (SMN1) leading to Spinal Muscular Atrophy (SMA): A computational approach.

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the mutations in survival motor neuron 1 gene (SMN1). The molecular pathology of missense mutations in SMN1 is not thoroughly investigated so far. Therefore, we collected all missense mutations in the SMN1 protein, using all possible search terms, from three databases (PubMed, PMC and Google Scholar). All missense mutations were subjected to in silico pathogenicity, conservation, and stability analysis tools. We used statistical analysis as a QC measure for validating the specificity and accuracy of these tools. PolyPhen-2 demonstrated the highest specificity and accuracy. While PolyPhen-1 showed the highest sensitivity; overall, PolyPhen2 showed better measures in comparison to other in silico tools. Three mutations (D44V, Y272C, and Y277C) were identified as the most pathogenic and destabilizing. Further, we compared the physiochemical properties of the native and the mutant amino acids and observed loss of H-bonds and aromatic stacking upon the cysteine to tyrosine substitution, which led to the loss of aromatic rings and may reduce protein stability. The three mutations were further subjected to Molecular Dynamics Simulation (MDS) analysis using GROMACS to understand the structural changes. The Y272C and Y277C mutants exhibited maximum deviation pattern from the native protein as compared to D44V mutant. Further MDS analysis predicted changes in the stability that may have been contributed due to the loss of hydrogen bonds as observed in intramolecular hydrogen bond analysis and physiochemical analysis. A loss of function/structural impact was found to be severe in the case of Y272C and Y277C mutants in comparison to D44V mutation. Correlating the results from in silico predictions, physiochemical analysis, and MDS, we were able to observe a loss of stability in all the three mutants. This combinatorial approach could serve as a platform for variant interpretation and drug design for spinal muscular dystrophy resulting from missense mutations.

A Molecular Docking and Dynamics Approach to Screen Potent Inhibitors Against Fosfomycin Resistant Enzyme in Clinical Klebsiella pneumoniae.

Klebsiella pneumoniae, BA6753 was cultured from a patient in the Clinical Microbiology Laboratory of Christian Medical College. K. pneumoniae, BA6753 has a multidrug resistance plasmid encoding novel FosA variant-7, fosfomycin resistance enzyme. Minimal side effects and a wide range of bactericidal activity of fosfomycin have resulted in its expanded clinical use that prompts the rise of fosfomycin-resistant strains. At present, there are no effective inhibitors available to conflict the FosA-medicated fosfomycin resistance. To develop effective FosA inhibitors, it is crucial to understand the structural and dynamic properties of resistance enzymes. Hence, the present study focuses on the identification of potent inhibitors that can effectively bind to the fosfomycin resistance enzyme, thus predispose the target to inactivate by the second antibiotic. Initially, a series of active compounds were screened against the resistant enzyme, and the binding affinities were confirmed using docking simulation analysis. For efficient activity, the binding affinity of the resistance enzyme ought to be high with the inhibitor than the fosfomycin drug. Consequently, the enzyme-ligand complex which showed higher binding affinity than the fosfomycin was employed for subsequent analysis. The stability of the top scoring enzyme-ligand complex was further validated using molecular dynamics simulation studies. On the whole, we presume that the compound 19583672 demonstrates a higher binding affinity for the resistance enzyme comparing to other compounds and fosfomycin. We believe that further enhancement of the lead compound can serve as a potential inhibitor against resistance enzyme in drug discovery process. J. Cell. Biochem. 118: 4088-4094, 2017. © 2017 Wiley Periodicals, Inc.

Comparative computational assessment of the pathogenicity of mutations in the Aspartoacylase enzyme.

Aspartoacylase (ASPA) is a zinc-dependent abundant enzyme in the brain, which catalyzes the conversion of N-acetyl aspartate (NAA) into acetate and aspartate. Mutations in the ASPA gene are associated with the development of Canavan disease (CD), leading to the deficiency of ASPA activity. Patients with CD were characterized by degeneration of the white matter of the brain. We reported earlier on two patients with severe form of CD that both had two novel missense mutations in the ASPA: c.427 A > G; p. I143V and c.557 T > A; p. V186D (Zaki et al. 2017a), patient 1 harbored both mutations (p.I143V and p.V186D) in a heterozygous form together with four other mutations, and patient 2 had both mutations in homozygous form. Wijayasinghe et al. (2014) crystallized the 3D structures of four different ASPA mutants (p.K213E, p.Y231C, p.E285A, and p.F295S). In this study, we used in silico prediction methods and molecular dynamics simulation (MDS) to understand the structural impact of all these mutations. Moreover, we used molecular docking (MD) to investigate the binding patterns of the NAA substrate to the native and mutant proteins. Among the mutations, p.E285A (crystallized mutant) was predicted to be the most deleterious for the protein function and the least deleteriousness mutant was the p.I143V (novel mutant). Among the novel mutations, p.V186D was observed to be disruptive for both the zinc binding and NAA binding than the p.I143V. This study provides practical insights on the effect of these mutations on the ASPA function and might serve as a platform for drug design for CD treatment.

Two patients with Canavan disease and structural modeling of a novel mutation.

Canavan disease (CD) is a rare fatal childhood neurological autosomal recessive genetic disease caused by mutations in the ASPA gene, which lead to catalytic deficiency of the ASPA enzyme, which catalyzes the hydrolysis of N-acetyl-L-aspartate (NAA) into aspartate and acetate. CD occurs frequently among Ashkenazi Jewish population, however it has been reported in many other ethnic groups with significantly lower frequency. Here, we report on two Egyptian patients diagnosed with CD, the first patient harbors five missense mutations (c.427 A > G; p. I143V, c.502C > T; p. R168C, c.530 T > C; p. I177T, c.557 T > C; p. V186D c.548C > T; p. P183L) and a silent mutation (c.693 C > T; p. Y231Y). The second patient was found to be homozygous for two missense mutations (c.427 A > G; p. I143V and c.557 T > A; p. V186D). Furthermore, molecular modeling of the novel mutation p. P183L provides an instructive explanation of the mutational impact on the protein structure that can affect the function of the ASPA. Here, the clinical, radiological, and biochemical profile of the two patients are reviewed in details.

Impact of I30T and I30M substitution in MPZ gene associated with Dejerine-Sottas syndrome type B (DSSB): A molecular modeling and dynamics.

Myelin protein zero (MPZ) gene encodes MPZ protein is a vital component of the myelin sheath. Mutationsassociated with MPZ gene leads to severe de-hypomyelination Dejerine-Sottas syndrome type B (DSSB) also termed as Charcot-Marie-Tooth disease (CMT) type 3. In this work, we employed a set of various in silico prediction methods to screen 97 nsSNPs associated with MPZ gene. Based on this, we identified the nsSNPs to be most deleterious and pathogenic associated with DSSB. To get more insight into the mutational effect at three-dimensional structural level, we modeled the homology structure of native type as well as I30T and I30M mutant of MPZ protein using Modeler 9.13 software. Molecular dynamics simulation was initiated to explain the impact of the mutation on its structure and function. The obtained results depict that the protein with I30T mutation had variable structural conformation and dynamic behavior than native and mutant I30M of MPZ protein. We hope our computational insight might be helpful in rationalizing the deleterious mutations in DSSB and the advancement of novel pharmacological strategy.

Immobilization of ß-galactosidase from Lactobacillus plantarum HF571129 on ZnO nanoparticles: characterization and lactose hydrolysis.

ß-Galactosidase from Lactobacillus plantarum HF571129 was immobilized on zinc oxide nanoparticles (ZnO NPs) using adsorption and cross-linking technique. Immobilized ß-galactosidase showed broad-spectrum pH optima at pH 5-7.5 and temperature 50-60 °C. Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM) showed that ß-galactosidase successfully immobilized onto supports. Due to the limited diffusion of high molecular weight substrate, K m of immobilized enzyme slightly increased from 6.64 to 10.22 mM, while V max increased from 147.5 to 192.4 µmol min(-1) mg(-1) as compared to the soluble enzyme. The cross-linked adsorbed enzyme retained 90 % activity after 1-month storage, while the native enzyme showed only 74 % activity under similar incubation conditions. The cross-linked ß-galactosidase showed activity until the seventh cycle and maintained 88.02 % activity even after the third cycle. The activation energy of thermal deactivation from immobilized biocatalyst was 24.33 kcal/mol with a half-life of 130.78 min at 35 °C. The rate of lactose hydrolysis for batch and packed bed was found to be 0.023 and 0.04 min(-1).