Molecular dynamics simulations have been used extensively to model the folding and unfolding of proteins. The rates of folding and unfolding should follow the Arrhenius equation over a limited range of temperatures. This study shows that molecular dynamic simulations of the unfolding of crambin between 500K and 560K do follow the Arrhenius equation. They also show that while there is a large amount of variation between the simulations the average values for the rate show a very high degree of correlation.
Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis.
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.
Non-obstructive azoospermia is a severe infertility factor. Currently, the etiology of this condition remains elusive with several possible molecular pathway disruptions identified in the post-meiotic spermatozoa. In the presented study, in order to identify all possible candidate genes associated with azoospermia and to map their relationship, we present the first protein-protein interaction network related to azoospermia and analyze the complex effects of the related genes systematically. Using Online Mendelian Inheritance in Man, the Human Protein Reference Database and Cytoscape, we created a novel network consisting of 209 protein nodes and 737 interactions. Mathematical analysis identified three proteins, ar, dazap2, and esr1, as hub nodes and a bottleneck protein within the network. We also identified new candidate genes, CREBBP and BCAR1, which may play a role in azoospermia. The gene ontology analysis suggests a genetic link between azoospermia and liver disease. The KEGG analysis also showed 45 statistically important pathways with 31 proteins associated with colorectal, pancreatic, chronic myeloid leukemia and prostate cancer. Two new genes and associated diseases are promising for further experimental validation.
Complete elucidation of fertilization process at molecular level is one of the unresolved challenges in sexual reproduction studies, and understanding the molecular mechanism is crucial in overcoming difficulties in infertility and unsuccessful in vitro fertilization. Sperm-oocyte interaction is one of the most remarkable events in fertilization process, and deficiency in protein-protein interactions which mediate this interaction is a major cause of unexplained infertility. Due to detection of how the various defects of sperm-oocyte interaction can affect fertilization failure, different experimental methods have been applied. This review summarizes the current understanding of sperm-egg interaction mechanism during fertilization and also accumulates the different types of sperm-egg interaction abnormalities and their association with infertility. Several detection approaches regarding sperm-egg protein interactions and the associated defects are reviewed in this paper.
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.
Xylitol is a high-value low-calorie sweetener used as sugar substitute in food and pharmaceutical industry. Xylitol phosphate dehydrogenase (XPDH) catalyses the conversion of d-xylulose 5-phosphate (XU5P) and d-ribulose 5-phosphate (RU5P) to xylitol and ribitol respectively in the presence of nicotinamide adenine dinucleotide hydride (NADH). Although these enzymes have been shown to produce xylitol and ribitol, there is an incomplete understanding of the mechanism of the catalytic events of these reactions and the detailed mechanism has yet to be elucidated. The main goal of this work is to analyse the conformational changes of XPDH-bound ligands such as zinc, NADH, XU5P, and RU5P to elucidate the key amino acids involved in the substrate binding. In silico modelling, comparative molecular dynamics simulations, interaction analysis and conformational study were carried out on three XPDH enzymes of the Medium-chain dehydrogenase (MDR) family in order to elucidate the atomistic details of conformational transition, especially on the open and closed state of XPDH. The analysis also revealed the possible mechanism of substrate specificity that are responsible in the catalyse hydride transfer are the residues His58 and Ser39 which would act as the proton donor for reduction of XU5P and RU5P respectively. The structural comparison and MD simulations displayed a significant difference in the conformational dynamics of the catalytic and coenzyme loops between Apo and XPDH-complexes and highlight the contribution of newly found triad residues. This study would assist future mutagenesis study and enzyme modification work to increase the catalysis efficiency of xylitol production in the industry.
Previous molecular dynamic simulations have reported elongation of the existing beta-sheet in prion proteins. Detailed examination has shown that these elongations do not extend beyond the proline residues flanking these beta-sheets. In addition, proline has also been suggested to possess a possible structural role in preserving protein interaction sites by preventing invasion of neighboring secondary structures. In this work, we have studied the possible structural role of the flanking proline residues by simulating mutant structures with alternate substitution of the proline residues with valine. Simulations showed a directional inhibition of elongation, with the elongation progressing in the direction of valine including evident inhibition of elongation by existing proline residues. This suggests that the flanking proline residues in prion proteins may have a containment role and would confine the beta-sheet within a specific length.
Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new computationally resolved interactions and the genetic links between sperm-egg interaction abnormalities and the associated disease.
Sperm-egg interaction is one of the most impressive processes in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization. The main purpose of this study is to map the sperm-egg interaction network between cell-surface proteins and perform an interaction analysis on this new network. We built the first protein interaction network of human sperm-egg binding and fusion proteins that consists of 84 protein nodes and 112 interactions. The gene ontology analysis identified a number of functional clusters that may be involved in the sperm-egg interaction. These include G-protein coupled receptor protein signaling pathway, cellular membrane fusion, and single fertilization. The PPI network showed a highly interconnected network and identified a set of candidate proteins: ADAM-ZP3, ZP3-CLGN, IZUMO1-CD9, and ADAM2-IZUMO1 that may have an important role in sperm-egg interaction. The result showed that the ADAM2 may mediate interaction between two essential factors CD9 and IZUMO1. The KEGG analysis showed 12 statistically significant pathways with 10 proteins associated with cancer, suggesting a common pathway between tumor fusion and sperm-egg fusion. We believe that the availability of this map will assist future researches in the fertilization mechanism and will also facilitate biological interpretation of sperm-egg interaction.
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.
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.
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 0.35gl(-1) and 1.40gl(-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.
The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.
Computational approaches to the disulphide bonding state and its connectivity pattern prediction are based on various descriptors. One descriptor is the amino acid sequence motifs flanking the cysteine residue motifs. Despite the existence of disulphide bonding information in many databases and applications, there is no complete reference and motif query available at the moment. Cysteine motif database (CMD) is the first online resource that stores all cysteine residues, their flanking motifs with their secondary structure, and propensity values assignment derived from the laboratory data. We extracted more than 3 million cysteine motifs from PDB and UniProt data, annotated with secondary structure assignment, propensity value assignment, and frequency of occurrence and coefficiency of their bonding status. Removal of redundancies generated 15875 unique flanking motifs that are always bonded and 41577 unique patterns that are always nonbonded. Queries are based on the protein ID, FASTA sequence, sequence motif, and secondary structure individually or in batch format using the provided APIs that allow remote users to query our database via third party software and/or high throughput screening/querying. The CMD offers extensive information about the bonded, free cysteine residues, and their motifs that allows in-depth characterization of the sequence motif composition.
Longimonas halophila and Longibacter salinarum are type strains of underexplored genera affiliated with Salisaetaceae Herein, we report the draft genome sequences of two strains of these bacteria, L. halophila KCTC 42399 and L. salinarum KCTC 52045, with the intent of broadening knowledge of this family. Genome annotation and gene mining revealed that both bacteria exhibit amylolytic abilities.
Psychrophiles are cold-living microorganisms synthesizing enzymes that are permanently active at almost near-zero temperatures. Psychrozymes are supposed to be structurally more flexible than their homologous proteins. This structural flexibility enables these proteins to undergo conformational changes during catalysis and improve catalytic efficiency at low temperatures. The outstanding characteristics of the psychrophilic enzymes have attracted the attention of the scientific community to utilize them in a wide variety of industrial and pharmaceutical applications. In this review, we first highlight the current knowledge of the cold-adaptation mechanisms of the psychrophiles. In the sequel, we describe the potential applications of the enzymes in different biotechnological processes specifically, in the production of industrial and pharmaceutical products. KEY POINTS: • Methods that organisms have evolved to survive and proliferate at cold environments. • The economic benefits due to their high activity at low and moderate temperatures. • Applications of the psychrophiles in biotechnological and pharmaceutical industry.
Dehalogenases are of high interest due to their potential applications in bioremediation and in synthesis of various industrial products. DehL is an L-2-haloacid dehalogenase (EC 3.8.1.2) that catalyses the cleavage of halide ion from L-2-halocarboxylic acid to produce D-2-hydroxycarboxylic acid. Although DehL utilises the same substrates as the other L-2-haloacid dehalogenases, its deduced amino acid sequence is substantially different (<25%) from those of the rest L-2-haloacid dehalogenases. To date, the 3D structure of DehL is not available. This limits the detailed understanding of the enzyme's reaction mechanism. The present work predicted the first homology-based model of DehL and defined its active site. The monomeric unit of the DehL constitutes α/β structure that is organised into two distinct structural domains: main and subdomains. Despite the sequence disparity between the DehL and other L-2-haloacid dehalogenases, its structural model share similar fold as the experimentally solved L-DEX and DehlB structures. The findings of the present work will play a crucial role in elucidating the molecular details of the DehL functional mechanism.
birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.