Mohd Shahir Shamsir

Molecular Modelling and Functional Studies of the Non-Stereospecific α-Haloalkanoic Acid Dehalogenase (DehE) from Rhizobium SP. RC1 and its Association with 3-Chloropropionic Acid (β-Chlorinated Aliphatic Acid)

ABSTRACT Many environmental pollutions are caused by the abundance of xenobiotic compounds in nature. For instance, halogenated compounds released from chemical industries were proven to be toxic and recalcitrant in the environment. However, haloalkanoic acid dehalogenases can catalyse the removal of halides from organic haloacids and thus have gained interest for bioremediation and synthesis of industrial chemicals. This study presents the first structural model and the key residues of the non-stereospecific haloalkanoic acid dehalogenase, DehE, from Rhizobium sp. RC1. The enzyme was built using a homology modelling technique; the structure of DehI from Pseudomonas putida PP3 was used as a template, because of its homology to DehE. The structure of DehE consists of only α-helices. Twelve conserved residues that line the active site were identified: Trp34, Ala36, Phe37, Asn114, Tyr117 Ala187, Ser188, Asp189, Tyr265, Phe268, Ile269, and Ile272. These residues are consistent with the residues found in the active site of DehI and D, L-DEX 113 from Pseudomonas sp. 113. Asp189 activates the water molecule as a nucleophile to attack the substrate chiral centre, which would result in an inversion of configuration of either D- or L-substrates. Both D- and L-substrates bind to and interact with the enzyme by hydrogen bonding with three residues, Trp34, Phe37, and Ser188. In addition, a putative tunnel was also identified that would provide a channel for the substrate to access the binding site. Based on computational analysis, DehE was proven to have the substrate affinity towards 3-chloropropionic acid (3CP)/β-chlorinated aliphatic acid, however, its dehalogenation process is far from clear. This DehE structural information will allow for rational design of non-stereospecific haloalkanoic acid dehalogenases in the future.

Crystal structure of Anoxybacillus α-amylase provides insights into maltose binding of a new glycosyl hydrolase subclass

A new subfamily of glycosyl hydrolase family GH13 was recently proposed for α-amylases from Anoxybacillus species (ASKA and ADTA), Geobacillus thermoleovorans (GTA, Pizzo, and GtamyII), Bacillus aquimaris (BaqA), and 95 other putative protein homologues. To understand this new GH13 subfamily, we report crystal structures of truncated ASKA (TASKA). ASKA is a thermostable enzyme capable of producing high levels of maltose. Unlike GTA, biochemical analysis showed that Ca2+ ion supplementation enhances the catalytic activities of ASKA and TASKA. The crystal structures reveal the presence of four Ca2+ ion binding sites, with three of these binding sites are highly conserved among Anoxybacillus α-amylases. This work provides structural insights into this new GH13 subfamily both in the apo form and in complex with maltose. Furthermore, structural comparison of TASKA and GTA provides an overview of the conformational changes accompanying maltose binding at each subsite.

Model-assisted formate dehydrogenase-O (fdoH) gene knockout for enhanced succinate production in Escherichia coli from glucose and glycerol carbon sources

Succinic acid is an important platform chemical that has broad applications and is been listed as one of the top twelve bio-based chemicals produced from biomass by the US Department of Energy. The metabolic role of Escherichia coli formate dehydrogenase-O (fdoH) under anaerobic conditions in relation to succinic acid production remained largely unspecified. Herein we report, what are to our knowledge, the first metabolic fdoH gene knockout that have enhanced succinate production using glucose and glycerol substrates in E. coli. Using the most recent E. coli reconstruction iJO1366, we engineered its host metabolism to enhance the anaerobic succinate production by deleting the fdoH gene, which blocked H+ conduction across the mutant cell membrane for the enhanced succinate production. The engineered mutant strain BMS4 showed succinate production of 2.05 g l−1 (41.2-fold in 7 days) from glycerol and .39 g l−1 (6.2-fold in 1 day) from glucose. This work revealed that a single deletion of the fdoH gene is sufficient to increase succinate production in E. coli from both glucose and glycerol substrates.

Bioinformatics in Malaysia: Hope, Initiative, Effort, Reality, and Challenges

The published articles in PLoS Computational Biology on the development of computational biology research in Mexico, Brazil, Cuba, Costa Rica, and Thailand have inspired us to report on the development of bioinformatics activities in Malaysia. Rapid progress in molecular biology research and biotechnology in Malaysia has created sufficient demand for bioinformatics in Malaysia. Although bioinformatics in Malaysia started in the early 1990s, the initial focus on the development of the biotechnology industry has curtailed the early gains and overshadowed the systematic development of bioinformatics in Malaysia, which currently lacks in human capital development, research, and commercialization. However, government initiatives have been devised to develop the necessary national bioinformatics network and human resource development programs and to provide the necessary infrastructure, connectivity, and resources for bioinformatics. Stakeholders are experiencing reorientation and consolidating existing strengths to align with the global trends in bioinformatics. This exercise is expected to reinvigorate the bioinformatics industry in Malaysia. Tapping into niche expertise and resources such as biodiversity and coupling it with the existing biotechnology infrastructure will help to create sustainable development momentum for the future. An initiative arose from several senior scientists across local universities in Malaysia to promote this new scientific discipline in the country.

Model-aided atpE gene knockout strategy in Escherichia coli for enhanced succinic acid production from glycerol

Succinic acid is an important platform chemical with a variety of applications. Model-guided metabolic engineering strategies in Escherichia coli for strain improvement to increase succinic acid production using glucose and glycerol remain largely unexplored. Herein, we report what are, to our knowledge, the first metabolic knockout of the atpE gene to have increased succinic acid production using both glucose and alternative glycerol carbon sources in E. coli. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic production of succinic acid by deleting the atpE gene, thereby generating additional reducing equivalents by blocking H+ conduction across the mutant cell membrane. This strategy produced 1.58 and .49 g l−1 of succinic acid from glycerol and glucose substrate, respectively. This work further elucidates a model-guided and/or system-based metabolic engineering, involving only a single-gene deletion strategy for enhanced succinic acid production in E. coli.

Structure Prediction, Molecular Dynamics Simulation and Docking Studies of D-Specific Dehalogenase from Rhizobium sp. RC1

Currently, there is no three-dimensional structure of D-specific dehalogenase (DehD) in the protein database. We modeled DehD using ab initio technique, performed molecular dynamics (MD) simulation and docking of D-2-chloropropionate (D-2CP), D-2-bromopropionate (D-2BP), monochloroacetate (MCA), monobromoacetate (MBA), 2,2-dichloropropionate (2,2-DCP), D,L-2,3-dichloropropionate (D,L-2,3-DCP), and 3-chloropropionate (3-CP) into the DehD active site. The sequences of DehD and D-2-haloacid dehalogenase (HadD) from Pseudomonas putida AJ1 have 15% sequence similarity. The model had 80% of the amino acid residues in the most favored region when compared to the crystal structure of DehI from Pseudomonas putida PP3. Docking analysis revealed that Arg107, Arg134 and Tyr135 interacted with D-2CP, and Glu20 activated the water molecule for hydrolytic dehalogenation. Single residue substitutions at 25–30 °C showed that polar residues of DehD were stable when substituted with nonpolar residues and showed a decrease in activity within the same temperature range. The molecular dynamics simulation of DehD and its variants showed that in R134A variant, Arg107 interacted with D-2CP, while in Y135A, Gln221 and Arg231 interacted with D-2CP. It is our emphatic belief that the new model will be useful for the rational design of DehDs with enhanced potentials.

Effects of Physiochemical Factors on Prokaryotic Biodiversity in Malaysian Circumneutral Hot Springs

Malaysia has a great number of hot springs, especially along the flank of the Banjaran Titiwangsa mountain range. Biological studies of the Malaysian hot springs are rare because of the lack of comprehensive information on their microbial communities. In this study, we report a cultivation-independent census to describe microbial communities in six hot springs. The Ulu Slim (US), Sungai Klah (SK), Dusun Tua (DT), Sungai Serai (SS), Semenyih (SE), and Ayer Hangat (AH) hot springs exhibit circumneutral pH with temperatures ranging from 43°C to 90°C. Genomic DNA was extracted from environmental samples and the V3–V4 hypervariable regions of 16S rRNA genes were amplified, sequenced, and analyzed. High-throughput sequencing analysis showed that microbial richness was high in all samples as indicated by the detection of 6,334–26,244 operational taxonomy units. In total, 59, 61, 72, 73, 65, and 52 bacterial phyla were identified in the US, SK, DT, SS, SE, and AH hot springs, respectively. Generally, Firmicutes and Proteobacteria dominated the bacterial communities in all hot springs. Archaeal communities mainly consisted of Crenarchaeota, Euryarchaeota, and Parvarchaeota. In beta diversity analysis, the hot spring microbial memberships were clustered primarily on the basis of temperature and salinity. Canonical correlation analysis to assess the relationship between the microbial communities and physicochemical variables revealed that diversity patterns were best explained by a combination of physicochemical variables, rather than by individual abiotic variables such as temperature and salinity.

Model-guided metabolic gene knockout of gnd for enhanced succinate production in Escherichia coli from glucose and glycerol substrates

The metabolic role of 6-phosphogluconate dehydrogenase (gnd) under anaerobic conditions with respect to succinate production in Escherichia coli remained largely unspecified. Herein we report what are to our knowledge the first metabolic gene knockout of gnd to have increased succinic acid production using both glucose and glycerol substrates in E. coli. Guided by a genome scale metabolic model, we engineered the E. coli host metabolism to enhance anaerobic production of succinic acid by deleting the gnd gene, considering its location in the boundary of oxidative and non-oxidative pentose phosphate pathway. This strategy induced either the activation of malic enzyme, causing up-regulation of phosphoenolpyruvate carboxylase (ppc) and down regulation of phosphoenolpyruvate carboxykinase (ppck) and/or prevents the decarboxylation of 6 phosphogluconate to increase the pool of glyceraldehyde-3-phosphate (GAP) that is required for the formation of phosphoenolpyruvate (PEP). This approach produced a mutant strain BMS2 with succinic acid production titers of 0.35 g l(-1) and 1.40 g l(-1) from glucose and glycerol substrates respectively. This work further clearly elucidates and informs other studies that the gnd gene, is a novel deletion target for increasing succinate production in E. coli under anaerobic condition using glucose and glycerol carbon sources. The knowledge gained in this study would help in E. coli and other microbial strains development for increasing succinate production and/or other industrial chemicals.

In silico deletion of PtsG gene in Escherichia coli genome-scale model predicts increased succinate production from glycerol

Systems metabolic engineering and in silico analyses are necessary to study gene knockout candidate for enhanced succinic acid production by Escherichia coli. Metabolically engineered E. coli has been reported to produce succinate from glucose and glycerol. However, investigation on in silico deletion of ptsG/b1101 gene in E. coli from glycerol using minimization of metabolic adjustment algorithm with the OptFlux software platform has not yet been elucidated. Herein we report what is to our knowledge the first direct predicted increase in succinate production following in silico deletion of the ptsG gene in E. coli GEM from glycerol with the OptFlux software platform. The result indicates that the deletion of this gene in E. coli GEM predicts increased succinate production that is 20% higher than the wild-type control model. Hence, the mutant model maintained a growth rate that is 77% of the wild-type parent model. It was established that knocking out of the ptsG/b1101 gene in E. coli using glucose as substrate enhanced succinate production, but the exact mechanism of this effect is still obscure. This study informs other studies that the deletion of ptsG/b1101 gene in E. coli GEM predicted increased succinate production, enabling a model-driven experimental inquiry and/or novel biological discovery on the underground metabolic role of this gene in E. coli central metabolism in relation to increasing succinate production when glycerol is the substrate.

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway

This paper introduces an improved Differential Evolution algorithm (IDE) which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE) in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than Genetic Algorithm (GA) and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms