Dimensional Structure Of Protein Research Articles

Protein Denaturation on p-T Axes--Thermodynamics and Analysis.

Proteins are essential players in the vast majority of molecular level life processes. Since their structure is in most cases substantial for their correct function, study of their structural changes attracted great interest in the past decades. The three dimensional structure of proteins is influenced by several factors including temperature, pH, presence of chaotropic and cosmotropic agents, or presence of denaturants. Although pressure is an equally important thermodynamic parameter as temperature, pressure studies are considerably less frequent in the literature, probably due to the technical difficulties associated to the pressure studies. Although the first steps in the high-pressure protein study have been done 100 years ago with Bridgman's ground breaking work, the field was silent until the modern spectroscopic techniques allowed the characterization of the protein structural changes, while the protein was under pressure. Recently a number of proteins were studied under pressure, and complete pressure-temperature phase diagrams were determined for several of them. This review summarizes the thermodynamic background of the typical elliptic p-T phase diagram, its limitations and the possible reasons for deviations of the experimental diagrams from the theoretical one. Finally we show some examples of experimentally determined pressure-temperature phase diagrams.

Molecular Modelling in MRI Contrast Agents Interacting with Water Molecules: Hierarchical Clustering Method for Molecular Dynamics Data Analysis

A revised method to compute mean residence time (MRT) from molecular dynamics (MD) simulations is reported. One of the most frequent scenarios in the modeling of biological related systems is to describe their interactions with the solvent that in most of the cases is water. This is important, for instance, in determining the three dimensional structure and function of proteins that will greatly depend on the affinity of certain sections of these biomolecules with water. The amount of interaction with water can be quantified by means of the so called MRT, which for this specific case is the average time that a water molecule stays within a certain distance (threshold) from a particular referential point of the model molecule.

Cloning, Overexpression, and Purification of PhoR CytoplasmicDomain Protein from Mycobacterium tuberculosis strain H37Rv

Tuberculosis still becomesa major health problem in the world. This infectious disease is caused by Mycobacterium tuberculosis (Mtb). Novel anti-tubercular drug is urgently needed to counter multidrug resistant cases and Mtb's spread. The cytoplasmic domain of PhoR histidine kinase, a part of the two-component system PhoR-PhoP in Mtb, is one of the potential candidates for anti-tubercular drug target.Three dimensional protein of drug target is needed to screen potential drug candidate using rational drug design approaches. Previous studies have successfully characterized and isolated putative cytoplasmic domain of PhoR (CytoPhoR) from Mtb strain H37Rv. This study aimed to clone, overexpress and purify of CytoPhoR protein. CytoPhoR was fused with thioredoxin protein in expression vector pET32b and overexpressed in Escherichia coli ( E.coli )BL21 (DE3) as soluble fraction by induction 1 mM IPTG. Purification of his-tagged CytoPhoR was carried out using IMAC Ni-NTA Agarose his-tag affinity column. SDS-PAGE analysis showed that another protein was co-purified (~35 kDa) along with the CytoPhoR protein. Subsequent protein purification using DEAE-ion exchange column generate a strong single band of 37 kDa on SDS–PAGE which is indicated as CytoPhoR protein. The purified CytoPhoR protein was successfully obtained and can be used for further analysis on determining three dimensional structure of CytoPhoR protein.

Homology modeling and molecular docking studies of ArnA protein from Erwinia amylovora: role in polymyxin antibiotic resistance

The resistivity of plant pathogen Erwinia amylovora against the polymyxin group of antibiotics is enhanced by modification of lipid A from lipopolysaccharide with 4-amino-4-deoxy L-arabinose (Ara4N) catalyzed by a bifunctional protein ArnA. ArnA is the first enzyme in the lipid A modification pathway with distinct dehydrogenase and transformylase domains which has been known in development of resistivity to polymyxin group of antibiotics. Thus, three dimensional structure of ArnA protein from Erwinia amylovora was constructed using homology modeling technique. The quality and reliability of the generated 3-D model was then assessed by different online available programs such as What if, PROCHECK, QMEAN, ProSA along with superimposition by UCSF Chimera. Sequence analysis study of ArnA protein from E. coli, Erwinia amylovora, Yersinia pestis, Ps. aeruginosa and Salmonella showed conserved domains with exact active site residues. Molecular docking study of ArnA protein with substrate UDP-GlcA and different inhibitors such as 5-formyl-5,6,7,8 tetrahydrofolate, leucovorin and 5-methyl tetrahydrofolate revealed similar binding pocket. The residues ASN492, ARG510, SER433 and ARG619 of ArnA protein are involved in interactions with inhibitors. Thus, this study could be useful to understand the proper binding mode of inhibitors to inhibit the lipid A modification pathway of ArnA protein from plant pathogen Erwinia amylovora.

Computational prediction of candidate proteins for S-nitrosylation in Arabidopsis thaliana.

Nitric oxide (NO) is an important signaling molecule that regulates many physiological processes in plants. One of the most important regulatory mechanisms of NO is S-nitrosylation—the covalent attachment of NO to cysteine residues. Although the involvement of cysteine S-nitrosylation in the regulation of protein functions is well established, its substrate specificity remains unknown. Identification of candidates for S-nitrosylation and their target cysteine residues is fundamental for studying the molecular mechanisms and regulatory roles of S-nitrosylation in plants. Several experimental methods that are based on the biotin switch have been developed to identify target proteins for S-nitrosylation. However, these methods have their limits. Thus, computational methods are attracting considerable attention for the identification of modification sites in proteins. Using GPS-SNO version 1.0, a recently developed S-nitrosylation site-prediction program, a set of 16,610 candidate proteins for S-nitrosylation containing 31,900 S-nitrosylation sites was isolated from the entire Arabidopsis proteome using the medium threshold. In the compartments “chloroplast,” “CUL4-RING ubiquitin ligase complex,” and “membrane” more than 70% of the proteins were identified as candidates for S-nitrosylation. The high number of identified candidates in the proteome reflects the importance of redox signaling in these compartments. An analysis of the functional distribution of the predicted candidates showed that proteins involved in signaling processes exhibited the highest prediction rate. In a set of 46 proteins, where 53 putative S-nitrosylation sites were already experimentally determined, the GPS-SNO program predicted 60 S-nitrosylation sites, but only 11 overlap with the results of the experimental approach. In general, a computer-assisted method for the prediction of targets for S-nitrosylation is a very good tool; however, further development, such as including the three dimensional structure of proteins in such analyses, would improve the identification of S-nitrosylation sites.

Haemophilus influenzae Genome Database (HIGDB): A single point web resource for Haemophilus influenzae

BackgroundHaemophilus influenzae (H. Influenzae) is the causative agent of pneumonia, bacteraemia and meningitis. The organism is responsible for large number of deaths in both developed and developing countries. Even-though the first bacterial genome to be sequenced was that of H. Influenzae, there is no exclusive database dedicated for H. Influenzae. This prompted us to develop the Haemophilus influenzae Genome Database (HIGDB). MethodsAll data of HIGDB are stored and managed in MySQL database. The HIGDB is hosted on Solaris server and developed using PERL modules. Ajax and JavaScript are used for the interface development. ResultsThe HIGDB contains detailed information on 42,741 proteins, 18,077 genes including 10 whole genome sequences and also 284 three dimensional structures of proteins of H. influenzae. In addition, the database provides “Motif search” and “GBrowse”. The HIGDB is freely accessible through the URL: http://bioserver1.physics.iisc.ernet.in/HIGDB/. DiscussionThe HIGDB will be a single point access for bacteriological, clinical, genomic and proteomic information of H. influenzae. The database can also be used to identify DNA motifs within H. influenzae genomes and to compare gene or protein sequences of a particular strain with other strains of H. influenzae.

Automatic classification of protein structures using physicochemical parameters.

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

High pressure crystallization and crystallography of glucose isomerase

Crystallization of protein is still a "bottleneck" for the three-dimensional (3D) structure analysis of a protein molecule. Therefore, the study on the acceleration of the crystallization is important. Visuri et al. repoted, for the first time, that the crystallization of glucose isomerase (GI) crystals was significantly enhanced with increasing pressure [1], while they did not discuss further effects of pressure on the crystallization. We measured solubilities, growth rates of a crystal face, step velocities and two-dimensional (2D) nucleation rates, three-dimensional (3D) nucleation rates of the GI crystal under high pressure. From these results, we found the negative molar volume change of crystallization delta V, the decrease in a step ledge surface energy and the increase in a step kinetic coefficient with increasing pressure. Activation volume of crystallization delta V‡ was discussed and the absolute value of it was almost half of that of delta V [2]. X-ray crystallography is also very important to study how the three dimensional structure of protein is related to its function under high pressure at atomic level. Kundrot et al. analyzed the crystal structure of lysozyme at 100 MPa using a beryllium (Be) cell. The crystal was prepared under 0.1 MPa. Charron et al. reported that thaumatin crystals grown under 150 MPa were analyzed under 0.1 MPa. Although Kundrot et al. concluded that the protein structure under high pressure was compressed, they could not estimate the influence of the bond structure of the crystal at 0.1 MPa on the compressed structure. In Charron's case, the thaumatin structures in crystals prepared under 0.1 MPa and 150 MPa were almost identical, when they did X-ray analyses of both crystals at 0.1 MPa. From these two results, we considered that compression of the crystals was reversible. We conducted both Kundrot's and Charron's methods for GI using a stand-alone type Be vessel [3]. We found that (1) GI molecule was reversibly compressed under high pressure, (2) the distributions of water sites around GI molecules were almost identical in the range of pressures from 0.1 to 100 MPa.

In Vitro Evaluation of Influenza M2 and Leishmania major HSP70 (221-604) Chimer Protein.

Background:Permanent antigenic variation of influenza viruses causes a major concern to develop an effective human influenza vaccine. Conserved antigens are new vaccine candidates because it is not necessary to match the prepared vaccine with circulating strains. Ion channel M2 protein is conserved among all influenza A viruses, allowing the virus to enter host cells.Objectives:To prepare an effective vaccine against influenza A viruses, a chimerical DNA plasmid encoding Influenza virus M2 protein and Leishmania major HSP70 was constructed.Materials and Methods:Influenza A/New Caledonia/20/99 (H1N1) was inoculated into MDCK cell line and total RNA was extracted. The full length M2 gene was amplified by RT-PCR using designed specific primers, cloned into pGEM-T Easy cloning vector and completely sequenced. The M2 gene was then subcloned into the pcDNA upstream of HSP70 gene. Recombinant plasmids were transfected into COS-7 cells to evaluate protein expression.Results:The recombinant plasmids were confirmed by PCR, restriction enzyme analysis and sequencing. Three dimensional structure of chimer protein was assessed using specific software. Transient protein expression in eukaryotic cells was confirmed by specific mRNA detection, indirect Immunofluorescence test and western blotting.Conclusions:M2-HSP70 chimer protein was successfully expressed in eukaryotic cells. Computational studies of chimer peptide sequence revealed that fusing HSP to the C-terminal of M2 protein does not mask the predominant epitope of M2. HSP70 is a molecular chaperon and immunostimulatory component. Genetically fusing antigens to HSPs leads to the enrichment of DNA vaccine potency. The immunogenicity of this construct with different formulation would be evaluated in further investigations.

The structure and dynamics of BmR1 protein from Brugia malayi: in silico approaches.

Brugia malayi is a filarial nematode, which causes lymphatic filariasis in humans. In 1995, the disease has been identified by the World Health Organization (WHO) as one of the second leading causes of permanent and long-term disability and thus it is targeted for elimination by year 2020. Therefore, accurate filariasis diagnosis is important for management and elimination programs. A recombinant antigen (BmR1) from the Bm17DIII gene product was used for antibody-based filariasis diagnosis in “Brugia Rapid”. However, the structure and dynamics of BmR1 protein is yet to be elucidated. Here we study the three dimensional structure and dynamics of BmR1 protein using comparative modeling, threading and ab initio protein structure prediction. The best predicted structure obtained via an ab initio method (Rosetta) was further refined and minimized. A total of 5 ns molecular dynamics simulation were performed to investigate the packing of the protein. Here we also identified three epitopes as potential antibody binding sites from the molecular dynamics average structure. The structure and epitopes obtained from this study can be used to design a binder specific against BmR1, thus aiding future development of antigen-based filariasis diagnostics to complement the current diagnostics.

In Silico Three Dimensional Modelling of Mutant Glycogen Synthase Associated with Equine Type 1 Polysaccharide Storage Myopathy

IntroductionEquine Type 1 polysaccharide storage myopathy (PSSM1) is associated with a dominant, missense mutation in the equine glycogen synthase gene (GYS1) that increases the enzyme's activity. The amino acid sequence surrounding the PSSM1 mutation is highly conserved amongst species from yeast to horses suggesting that this region plays an important role, but its functional significance has yet to be determined.MethodsComputer modelling can predict three dimensional (3D) protein structures using related proteins as templates. Yeast glycogen synthase (GS) has high sequence homology (73%) to the equine enzyme and since the yeast enzyme's structure has been solved, we hypothesised that in silico modelling based on the yeast protein using MODELLER and SwissPDBViewer software would predict the enzyme's structure and explain the mutant enzyme's increased activity.ResultsHomology‐modelling revealed that, like the yeast enzyme, equine GS likely forms a tetramer surrounding a catalytic cleft and undergoes large structural rearrangements upon G6P binding to highly‐conserved binding sites. The enzyme's glycogen binding sites are located on the extremities of the enzyme and are also conserved in the equine protein. The PSSM1 mutation lay close to the G6P binding site, but did not confer any clear structural rearrangement on the protein's 3D configuration. The mutation was distant from the known phosphorylation sites and the glycogen binding sites.ConclusionsThe close proximity of the mutation to the G6P binding site suggests that it might influence substrate interaction or allosteric regulation. Further studies evaluating G6P ligand binding and enzyme activation are warranted and might best be made following purification of the mutant and wild type proteins.Ethical Animal ResearchNo animals were used in the study. Sources of funding: Petplan Charitable Trust and The Royal Veterinary College. Competing interests: none.

Glycated albumin: an overview of the In Vitro models of an In Vivo potential disease marker

Glycation is a general spontaneous process in proteins which has significant impact on their physical and functional properties. These changes in protein properties could be related to several pathological consequences such as cataract, arteriosclerosis and Alzheimer’s disease. Among the proteins, glycation of Human serum albumin (HSA) is of special interest. Human serum albumin is the most abundant protein in the plasma and because of its high sensitivity for glycation, undergoes structural and functional changes due to binding of reducing sugars in vitro. The glycation process occurs by plasma glucose in vivo which has great impacts on the three dimensional structure of protein. These changes are efficient and stable enough which makes the protein to be considered as a new special disease marker instead of HbA1C for diabetes. In some cases, glycated albumin was used as an alternative marker for glycemic control. Glycated albumin reacts with glucose ten times more rapidly than HbA1C and has shorter half-life which makes it more reliable for indicating glycemic states. In this review, glycation of Human Serum Albumin has been overviewed, starting from overall concepts of glycation, followed by some Examples of pathological consequences of protein glycation. The BSA aggregation was reviewed in terms of structural and biological impacts of glycation on the protein followed by reporting documents which indicate possibility of glycated albumin to be used as specific marker for diabetes. Finally, some of the studies related to the models of glycated albumin have been briefly described, with an emphasis on In vitro studies. It is interesting to note the relationship found between in vitro glycation experiments and the propensity of proteins to form amyloid structures, a point that could be further explored as to its significance in hyperglycemic states.

Amino acid contact energy networks impact protein structure and evolution

One of the most challenging tasks in structural proteomics is to understand the relationship between protein structure, biological function, and evolution. An understanding of amino acid networks based on protein topology has an important role in the study of this relationship; however, the relationship between network parameters underlying protein topology with structural properties or evolutionary rate is still unknown. To investigate this further, we modeled the three dimensional structure of proteins as amino acid contact energy networks (AACENs) with nodes represented as amino acid residues and edges established according to environment-dependent residue–residue contact energies. Five other types of networks were also constructed to investigate their topological parameters and compare their effect on protein structure and evolution: (1) a random contact network (RCN), (2) a rewiring network with the same degree of distribution as AACEN (RNDD), (3) long-range contact energy networks with and without the backbone connectivity (LCEN_BBs and LCENs), and (4) short range contact energy networks (SCENs). The results indicated that the long-range link percentage and the network clustering coefficient showed a significantly positive and negative correlation, respectively, with protein secondary structure density. In addition, the long-range link percentage and network diameter had a significantly positive and negative correlation, respectively, with evolutionary rate. According to our knowledge, this is the first study to identify the potential role of long-range links and network diameter in protein evolution.

<I>In Silico</I> Comparative Modeling of clpP2 Protein from (<I>Mycobacterium tuberculosis</I> H37Rv)

Tuberculosis remains one of the major threats to human health, Mycobacterium tuberculosis , a gram-positive bacterium kills two or three million people per year.ATPdependent Clp protease (ClpP) is a core unit of a major bacterial protease complex. The Mycobacterium tuberculosis (MTB) genome contains two genes coding for putative ClpPs, ClpP1 and ClpP2 respectively, that are likely to play a role in the virulence of the bacterium. ClpP2 employing as a new attractive drug target for that isolates ClpP2 involved in bacteria growth and oxidative stress tolerance. In the present study a complete structural analysis and 3-D modelling of clpP2 protein of Mycobacterium tuberculosis , was carried out. The 214 amino acid sequence of the clpP2 protein was retrieved from NCBI database. Based on the PDB Blast report, PDB file of ClpP from E. coli was used as template for three dimensional structure of the clpP2 protein, was predicted by using the software MODELLER 9v9.The template protein exposed 49% sequence identity with clpp2 protein. The computed model’s energy was minimized and validated using PROCHECK to obtain a stable model structure. Ramachandran plot showed that, fur protein have 94.7% fully allowed region, 5.3% additionally allowed region, 0.0% generously allowed region and 0.0% disallowed region and later then is submitted in Protein Model Database (PMDB-ID: PM0077929) . The model might be elucidated the use in additional functional characterization of this protein.

English

The Δ9-Tetrahydrocannabinol (THCA) is the primary psychoactive compound of Cannabis Sativa. It is produced by Δ1- Tetrahydrocannabinolic acid synthase (THCA) which catalyzes the oxidative cyclization of cannabigerolic acid (CBGA) the precursor of the THCA. In this study, we were interested by the three dimensional structure of THCA synthase protein. Generation of models were done by MODELLER v9.11 and homology modeling with Δ1-tetrahydrocannabinolic acid (THCA) synthase X ray structure (PDB code 3VTE) on the basis of sequences retrieved from GenBank. Procheck, Errat, and Verify 3D tools were used to verify the reliability of the six 3D models obtained, the overall quality factor and the Prosa Z-score were also used to check the quality of the six modeled proteins. The RMSDs for C-alpha atoms, main-chain atoms, side-chain atoms and all atoms between the modeled structures and the corresponding template ranged between 0.290 Å-1.252 Å, reflecting the good quality of the obtained models. Our study of the CBGA-THCA synthase docking demonstrated that the active site pocket was successfully recognized using computational approach. The interaction energy of CBGA computed in ‘fiber types’ proteins ranged between -4.1 95 kcal/mol and -5.95 kcal/mol whereas in the ‘drug type’ was about -7.02 kcal/mol to -7.16 kcal/mol, which maybe indicate the important role played by the interaction energy of CBGA in the determination of the THCA level in Cannabis Sativa L. varieties. Finally, we have proposed an experimental design in order to explore the binding energy source of ligand-enzyme in Cannabis Sativa and the production level of the THCA in the absence of any information regarding the correlation between the enzyme affinity and THCA level production. This report opens the doors to more studies predicting the binding site pocket with accuracy from the perspective of the protein affinity and THCA level produced in Cannabis Sativa.

Structural systems biology: from bacterial to cancer networks

A new research field of systems biology integrates available information about individual components of a biological system to construct a network model which can be analyzed and simulated to predict behavior of cells and organisms. At the same time, structural biology provides information about molecular, three dimensional structure of proteins, which are nodes in this network. However, these two fields historically developed separately and insights from the structures were not used to analyze networks, and vice versa, network information was now used to understand protein structures. We developed integrated, structural and network models of central metabolism in bacterial model systems, T. maritima and E. coli. This allowed us to study first the internal structure and history of the expansion of the network and then attempt quantitative predictions of the network failure in high temperatures. We are now applying similar integrated approach to model network consequences of cancer mutations, including effect of drug sensitivity on specific cancer cell lines.

Prosaposin regulates coenzyme Q10 levels in HepG2 cells, especially those in mitochondria.

Coenzyme Q10 (CoQ10) is a key component of the mitochondrial electron transfer chain and is one of the most important cellular antioxidants. We previously reported that glycoprotein saposin B (SapB) binds CoQ10 in human cells. To elucidate the physiological role of SapB and its precursor, prosaposin (Psap), we prepared stable transfectants of HepG2 that overexpress wild-type human Psap (Wt-Tf). We also established a SapB domain mutated Psap (Mt-Tf) in which cysteine198 was replaced with serine to disrupt three dimensional protein structure by the loss of S-S bridging. Psap knockdown (KD) strains were also examined. Western blotting analysis confirmed overexpression or knockdown of Psap in these HepG2 cells. The cellular ratios of CoQ10 to free cholesterol (FC) significantly decreased in the order of Wt-Tf>parental>Mt-Tf>KD. Additionally, the ratios of CoQ10/FC in mitochondrial fractions decreased in the order of Wt-Tf>parental>KD. These data indicate that Psap and/or SapB regulate CoQ10 levels in HepG2 cells, especially in their mitochondria.

Quality assessment methods for 3D protein structure models based on a residue-residue distance matrix prediction.

In the absence of experimentally determined three dimensional (3D) structures of proteins, the prediction of protein structures using computational methods is a standard alternative approach in bioinformatics. When using the predicted protein models to compute the native structure of an unknown target protein, estimating the actual quality of the protein models is important for selecting the best or near-best model. Moreover, estimates of the differences between the protein models and the native protein structure are obviously useful to end users who can then decide on the utility of the models for their specific problems. This article describes two new single-model quality assessment (QA) programs, pure single-model QA method (psQA) and a template based QA method (tbQA), that we developed. psQA is a pure single-model QA program that uses a neural network method to predict residue-residue distance matrices of the native protein structures. tbQA is a quasi-single-model QA program that mainly uses target-template sequence alignments and template structures. The performance of these two model QA programs was analyzed in a data set of 24022 models for 94 targets from the 10th critical assessment of protein structure prediction (CASP10) experiment.

A Nonadaptive Origin of a Beneficial Trait: In Silico Selection for Free Energy of Folding Leads to the Neutral Emergence of Mutational Robustness in Single Domain Proteins

Proteins are regarded as being robust to the deleterious effects of mutations. Here, the neutral emergence of mutational robustness in a population of single domain proteins is explored using computer simulations. A pairwise contact model was used to calculate the ΔG of folding (ΔG folding) using the three dimensional protein structure of leech eglin C. A random amino acid sequence with low mutational robustness, defined as the average ΔΔG resulting from a point mutation (ΔΔG average), was threaded onto the structure. A population of 1,000 threaded sequences was evolved under selection for stability, using an upper and lower energy threshold. Under these conditions, mutational robustness increased over time in the most common sequence in the population. In contrast, when the wild type sequence was used it did not show an increase in robustness. This implies that the emergence of mutational robustness is sequence specific and that wild type sequences may be close to maximal robustness. In addition, an inverse relationship between ∆∆G average and protein stability is shown, resulting partly from a larger average effect of point mutations in more stable proteins. The emergence of mutational robustness was also observed in the Escherichia coli colE1 Rop and human CD59 proteins, implying that the property may be common in single domain proteins under certain simulation conditions. The results indicate that at least a portion of mutational robustness in small globular proteins might have arisen by a process of neutral emergence, and could be an example of a beneficial trait that has not been directly selected for, termed a "pseudaptation."

Molecular characterization, modeling and expression analysis of a somatic embryogenesis receptor kinase (SERK) gene in Momordica charantia L. during somatic embryogenesis

Somatic embryogenesis receptor kinase (SERK) gene is known to be a marker of somatic embryogenesis in several plant species. The present study reported the isolation and characterization of a SERK gene ortholog, designated as McSERK, in Momordica charantia, an important medicinal plant. The complete coding region of McSERK was found to encode a 627 amino acid protein which contained an N-terminal signal peptide, a leucine zipper, five leucine rich repeats, a serine-proline-proline domain, a transmembrane domain, a kinase domain and the C-terminal region, depicting the typical characteristic features of SERK-family proteins. Phylogenetic analysis suggested that McSERK was highly similar to the SERK proteins of Cucumis sativus, Glycine max and Medicago truncatula. Homology modeling was attempted to construct the three dimensional structure of McSERK protein which showed that it corresponded to a monomeric protein. McSERK expression was high in embryogenic callus but its expression was relatively low in different plant organs. The high expression of McSERK transcript in the embryogenic callus confirmed its association with somatic embryogenesis in M. charantia.