Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon.

Synonymous codons translate into chemically identical amino acids. Once considered inconsequential to the formation of the protein product, there is now significant evidence to suggest that codon usage affects co-translational protein folding and the final structure of the expressed protein. Here we develop a method for computing and comparing codon-specific Ramachandran plots and demonstrate that the backbone dihedral angle distributions of some synonymous codons are distinguishable with statistical significance for some secondary structures. This shows that there exists a dependence between codon identity and backbone torsion of the translated amino acid. Although these findings cannot pinpoint the causal direction of this dependence, we discuss the vast biological implications should coding be shown to directly shape protein conformation and demonstrate the usefulness of this method as a tool for probing associations between codon usage and protein structure. Finally, we urge for the inclusion of exact genetic information into structural databases.

Identification and Computational Analysis of 3D-Structure Of MOCS1

Cyclic pyranopterin monophosphate (cPMP) is one of the most stable intermediates in Molybdenum cofactor (MoCo) biosynthetic pathway. In humans, synthesis of cPMP from Guanosine triphosphate (GTP) requires functional genes i.e. Molybdenum Cofactor Synthesis-1 (MOCS1) genes that contains for two catalytic proteins MOCS1A and MOCS1B. Importance of MOCS1A and MOCS1B for biosynthesis of MoCo reveals from the fact that its deficiency leads to MoCo type A deficiency. As there is no structure available for MOCS1 genes in the literature, tertiary structure of MOCS1 genes were investigated in this research via threading or folds recognition method by i-TASSER and validation was done using ERRAT, Verify3D and Ramachandran plots. Binding sites were predicted and validated. Docking of MOCS1A with GTP and MOCS1B with 3, 8 dihydroguanosine was done using Autodock via PyRx. Apart from this, highly confident mutations were also predicted using SIFT and polyphen2 that can alter the structure and function of MOCS1 gene.

Characterization of squalene synthase gene from Gymnema sylvestre R. Br.

Background Squalene synthase (SQS) is a rate-limiting enzyme necessary to produce pentacyclic triterpenes in plants. It is an important enzyme producing squalene molecules required to run steroidal and triterpenoid biosynthesis pathways working in competitive inhibition mode. Reports are available on information pertaining to SQS gene in several plants, but detailed information on SQS gene in Gymnema sylvestre R. Br. is not available. G. sylvestre is a priceless rare vine of central eco-region known for its medicinally important triterpenoids. Our work aims to characterize the GS-SQS gene in this high-value medicinal plant. Results Coding DNA sequences (CDS) with 1245 bp length representing GS-SQS gene predicted from transcriptome data in G. sylvestre was used for further characterization. The SWISS protein structure modeled for the GS-SQS amino acid sequence data had MolProbity Score of 1.44 and the Clash Score 3.86. The quality estimates and statistical score of Ramachandran plots analysis indicated that the homology model was reliable. For full-length amplification of the gene, primers designed from flanking regions of CDS encoding GS-SQS were used to get amplification against genomic DNA as template which resulted in approximately 6.2-kb sized single-band product. The sequencing of this product through NGS was carried out generating 2.32 Gb data and 3347 number of scaffolds with N50 value of 457 bp. These scaffolds were compared to identify similarity with other SQS genes as well as the GS-SQSs of the transcriptome. Scaffold_3347 representing the GS-SQS gene harbored two introns of 101 and 164 bp size. Both these intronic regions were validated by primers designed from adjoining outside regions of the introns on the scaffold representing GS-SQS gene. The amplification took place when the template was genomic DNA and failed when the template was cDNA confirmed the presence of two introns in GS-SQS gene in Gymnema sylvestre R. Br. Conclusion This study shows GS-SQS gene was very closely related to Coffea arabica and Gardenia jasminoides and this gene harbored two introns of 101 and 164 bp size.

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

In this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. α-helices and β-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.

3D Structure of VP1 Structural Protein on Enterovirus A71 Using Swiss-Model

Background: VP1 structural protein plays a role as a key player in the pathogenesis, has a uniqueness that is interesting enough to be studied by studying the nature and function of structural proteins VP1. This study aims to predict the three-dimensional structure of proteins VP1 on EV-A71. Methods: The target protein is obtained from UniProt server with an access code A0A097EV89using templates 4cey.1.A (PDB ID) were analyzed in silico by homology method using SWISS-MODEL server. Results: Analysis showed the target protein and the template has 95.29% identity and is composed of 297 amino acids with a value of -2.15 QMEAN. Structural protein VP1 in Ramachandran Plots have a stable structure, non-glycine residue in the outlier regions only around 0.34% (A53 ALA) Rated rotamer outliers 1.61%. Conclusion: The three-dimensional structure model of the protein studied has a stable structure and the information obtained is useful for further research in developing vaccines for diseases caused by EV-A71.

A global Ramachandran score identifies protein structures with unlikely stereochemistry

SummaryRamachandran plots report the distribution of the (φ, Ψ) torsion angles of the protein backbone and are one of the best quality metrics of experimental structure models. Typically, validation software reports the number of residues belonging to “outlier”, “allowed” and “favored” regions. While “zero unexplained outliers” can be considered the current “gold standard”, this can be misleading if deviations from expected distributions, even within the favored region, are not considered. We therefore revisited the Ramachandran Z-score (Rama-Z), a quality metric introduced more than two decades ago, but underutilized. We describe a re-implementation of the Rama-Z score in the Computational Crystallography Toolbox along with a new algorithm to estimate its uncertainty for individual models; final implementations are available both in Phenix and in PDB-REDO. We discuss the interpretation of the Rama-Z score and advocate including it in the validation reports provided by the Protein Data Bank. We also advocate reporting it alongside the outlier/allowed/favored counts in structural publications.

Design and expression of polytopic construct of cathepsin-L1, SAP-2 and FhTP16.5 proteins of Fasciola hepatica

The enzyme-linked immunosorbent assay (ELISA) technique can play an important role in the early detection of fascioliasis. However, they have some diagnostic limitations, including cross-reaction with other helminths. It seems that the combination of recombinant parasite proteins as antigen can reduce these problems. Hence, the present study was aimed to design and confirm the antigenic recombinant multi-epitope (rMEP) construct of three protein epitopes (linear and conformational B-cell epitopes) of the parasite using immunoinformatic tools. For this purpose, the tertiary structures of Fasciola hepatica cathepsin-L1, saposin-like protein 2 and 16.5-kDa tegument-associated protein were predicted using the I-TASSER server. Validation of the modelled structures was performed by Ramachandran plots. The antigenic epitopes of the proteins were achieved by analysing the features of the IEDB server. The synthesized gene was cloned into the pET-22b (+) expression vector and transformed into the Escherichia coli BL21. Sodium dodecyl sulfate polyacrylamide gel electrophoresis was used to verify and analyse the expression of the rMEP protein. Western blotting was utilized to confirm rMEP protein immunogenicity in two forms, one using an anti-His tag antibody and the other with human pooled sera samples (fascioliasis, non-fascioliasis and negative control sera). Our results demonstrated that the rMEP designed for the three proteins of F. hepatica was highly antigenic, and immune-detection techniques confirmed the antigen specificity. In conclusion, the presented antigenic multi-epitope may be very helpful to develop serodiagnostic kits such as indirect ELISA to evaluate the proper diagnosis of fascioliasis in humans and ruminants.

The BackMAP Python module: how a simpler Ramachandran number can simplify the life of a protein simulator

Protein backbones occupy diverse conformations, but compact metrics to describe such conformations and transitions between them have been missing. This report re-introduces the Ramachandran number (ℛ) as a residue-level structural metric that could simply the life of anyone contending with large numbers of protein backbone conformations (e.g., ensembles from NMR and trajectories from simulations). Previously, the Ramachandran number (ℛ) was introduced using a complicated closed form, which made the Ramachandran number difficult to implement. This report discusses a much simpler closed form of ℛ that makes it much easier to calculate, thereby making it easy to implement. Additionally, this report discusses how ℛ dramatically reduces the dimensionality of the protein backbone, thereby making it ideal for simultaneously interrogating large numbers of protein structures. For example, 200 distinct conformations can easily be described in one graphic using ℛ (rather than 200 distinct Ramachandran plots). Finally, a new Python-based backbone analysis tool—BackMAP—is introduced, which reiterates how ℛ can be used as a simple and succinct descriptor of protein backbones and their dynamics.