In silico comparative structural and functional analysis of arsenite methyltransferase from bacteria, fungi, fishes, birds, and mammals

BACKGROUND

Arsenic, a ubiquitous toxic metalloid, is a threat to the survival of all living organisms. Bioaccumulation of arsenic interferes with the normal physiological pathway. To overcome arsenic toxicity, organisms have developed arsenite methyltransferase enzyme, which methylates inorganic arsenite to organic arsenic MMA (III) in the presence of S-adenosylmethionine (SAM). Bacteria-derived arsM might be horizontally transported to different domains of life as arsM or as3mt (animal ortholog). A systematic study on the functional diversity of arsenite methyltransferase from various sources will be used in arsenic bioremediation.

RESULTS

Several arsenite methyltransferase protein sequences of bacteria, fungi, fishes, birds, and mammals were retrieved from the UniProt database. In silico physicochemical studies confirmed the acidic, hydrophilic, and thermostable nature of these enzymes. Interkingdom relationships were revealed by performing phylogenetic analysis. Homology modeling was performed by SWISS-MODEL, and that was validated through SAVES-v.6.0. QMEAN values ranged from - 0.93 to - 1.30, ERRAT score (83-96), PROCHECK (88-92%), and other parameters suggested models are statistically significant. MOTIF and PrankWeb discovered several functional motifs and active pockets within the proteins respectively. The STRING database showed protein-protein interaction networks.

CONCLUSION

All of our in silico studies confirmed the fact that arsenite methyltransferase is a cytosolic stable enzyme with conserved sequences over a wide range of organisms. Thus, because of its stable and ubiquitous nature, arsenite methyltransferase could be employed in arsenic bioremediation.

RSAD2: An exclusive target protein for Zika virus comparative modeling, characterization, energy minimization and stabilization

Objective

The major purpose of the present study was to predict the structure of Radical s-adenosyl-L-methionine Domain 2 (RSAD2), the most targeted protein of the Zika virus using comparative modeling, to validate the models that were generated and molecular dynamics (MD) simulations were performed.

Methods

The secondary structure of RSAD2 was estimated using the Garnier-Osguthorpe-Robson, Self-Optimized Prediction method with Alignment, and Position-Specific Iterative-Blast based secondary structure prediction algorithms. The best of them were preferred based on their DOPE score, then three-dimensional structure identification using SWISS-MODEL and the Protein Homology/Analogy Recognition Engine (Phyre2) server. SAVES 6.0 was used to validate the models, and the preferred model was then energetically stabilized. The model with least energy minimization was used for MD simulations using iMODS.

Results

The model predicted using SWISS-MODEL was determined as the best among the predicted models. In the Ramachandran plot, there were 238 residues (90.8%) in favored regions, 23 residues (8.8%) in allowed regions, and 1 residue (0.4%) in generously allowed regions. Energy minimization was calculated using Swiss PDB viewer, reporting the SWISS-MODEL with the lowest energy (E = -18439.475 KJ/mol) and it represented a stable structure conformation at three-dimensional level when analyzed by MD simulations.

Conclusion

A large amount of sequence and structural data is now available, for tertiary protein structure prediction, hence implying a computational approach in all the aspects becomes an opportunistic strategy. The best three-dimensional structure of RSAD2 was built and was confirmed with energy minimization, secondary structure validation and torsional angles stabilization. This modeled protein is predicted to play a role in the development of drugs against Zika virus infection.

Differential performance of RoseTTAFold in antibody modeling

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.

Solvent accessibility of E1α and E1β residues with known missense mutations causing pyruvate dehydrogenase complex (PDC) deficiency: Impact on PDC-E1 structure and function

Pyruvate dehydrogenase complex deficiency is a major cause of primary lactic acidemia resulting in high morbidity and mortality, with limited therapeutic options. PDHA1 mutations are responsible for >82% of cases. The E1 component of PDC is a symmetric dimer of heterodimers (αβ/α'β') encoded by PDHA1 and PDHB. We measured solvent accessibility surface area (SASA), utilized nearest-neighbor analysis, incorporated sequence changes using mutagenesis tool in PyMOL, and performed molecular modeling with SWISS-MODEL, to investigate the impact of residues with disease-causing missense variants (DMVs) on E1 structure and function. We reviewed 166 and 13 genetically resolved cases due to PDHA1 and PDHB, respectively, from variant databases. We expanded on 102 E1α and 13 E1β nonduplicate DMVs. DMVs of E1α Arg112-Arg224 stretch (exons 5-7) and of E1α Arg residues constituted 40% and 39% of cases, respectively, with invariant Arg349 accounting for 22% of arginine replacements. SASA analysis showed that 86% and 84% of residues with nonduplicate DMVs of E1α and E1β, respectively, are solvent inaccessible ("buried"). Furthermore, 30% of E1α buried residues with DMVs are deleterious through perturbation of subunit-subunit interface contact (SSIC), with 73% located in the Arg112-Arg224 stretch. E1α Arg349 represented 74% of buried E1α Arg residues involved in SSIC. Structural perturbations resulting from residue replacements in some matched neighboring pairs of amino acids on different subunits involved in SSIC at 2.9-4.0 Å interatomic distance apart, exhibit similar clinical phenotype. Collectively, this work provides insight for future target-based advanced molecular modeling studies, with implications for development of novel therapeutics for specific recurrent DMVs of E1α.

Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.

Correlation between TSC1 gene polymorphism and epilepsy

The correlation between tuberous sclerosis complex 1 (TSC1) gene polymorphism and epilepsy was studied. In total, 38 patients with epilepsy treated in People's Hospital of Rizhao from May 2015 to June 2016 were selected as study subjects, as the observation group, 38 healthy people in the same period were selected as the control group. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to study the polymorphism of TSC1 gene in the above study subjects. The mRNA expression of TSC1 gene in the observation group and the control group was measured by fluorescence quantitative PCR, the expression of TSC1 protein in the control and observation group was measured by western blotting and ELISA. The polymorphisms of TSC1 gene in control group and observation group were analyzed by PCR-RFLP. There were three genotypes of TCS1 gene locus 142 in healthy population: CC (79.3%), CA (13.9%) and AA (6.8%), there were also three genotypes at locus 142 in the observation group: CC (21.3%), CA (26.4%) and AA (52.3%), there was significant difference in the genotypes at locus 142 between healthy population and the patients with epilepsy (P<0.05). It was observed by fluorescence quantitative PCR that there was no significant difference in the mRNA expression of TSC1 gene between the control group and the observation group (P>0.05). The expression of TSC1 gene was detected by western blot method. Western blotting showed no significant difference in TSC1 protein expression between the two groups (P>0.05). However, by determining the activity of TSC1 protein in the observation group and the control group by ELISA, it was found that TSC1 activity in healthy human body (8.95±2.41 U/ml) was much lower than that in the patients with epilepsy (29.27±4.06 U/ml), the difference was statistically significant (P<0.05). It was found that locus 142 may be located at the active center of TSC1 enzyme by homology modeling of SWISS-MODEL, the mutation of locus 142 could lead to the change of TSC1 activity. The polymorphism of locus 142 in TSC1 gene is correlated with epilepsy, that is, the increase of CA and AA content in locus 142 leads to the occurrence of epilepsy.