Bioinformatic analysis of multi-drug resistant class 1 integron-coded protein of Citrobacter freundii

Background: The understanding of the secondary structure of the class 1 integron coded protein is necessary to decipher potential drug target and also to infer evolutionary ancestry at the proteomic level. This study was therefore aimed at determining the secondary structure of class 1 integron-coded protein and also to provide information on their evolutionary ancestry. Methodology: Five different sequences of Citrobacter freundii with the following accession numbers; KP902625.1, KP902624.1, KP902623.1, KP901093.1 and KP902609.1 were obtained using nucleotide BLAST (http://blast. ncbi.nlm.nih.gov/Blast.cgi) and subjected to evolutionary analysis, pairwise distance calculation, secondary structure and neutrality test using MEGA explorer, Kimura 2 parameter, SOPMA tool and Tajima’s test respectively. Results: Results of the NCBI queries revealed significant identity with class 1 integron of the studied Citrobacter freundii. The nucleotide sequence alignment depicted several conserved regions with varying degree of transitions, transversions, insertions, and deletions while the amino acid sequences of the nucleotides showed 42 conserved sites among all the sequences. The secondary structure of the class 1 integron coded protein depicted significant representation of the random coil (43.74±3.24), alpha helix (25.69±6.29) and the extended strands (22.42±2.41) than the beta turns (8.15±1.12). The Tajima’s Neutrality test of five nucleotide sequences of Citrobacter freundii analyzed by considering the first, second and third codons as well as the non-coding regions revealed a total of 127 positions in the final datasets while the Tajima’s Neutrality test was estimated to be -0.1038. Conclusion: The study confirmed common evolutionary ancestor for the class 1 integron coded protein found in Citrobacter freundii. Our study also documents the higher representation of random coil, alpha helix and extended strands than the beta turns. The negative value of the Tajima’s neutrality test suggests higher levels of both low and high frequency polymorphisms thus indicating a decrease in the class 1 integron population size and balancing selection Keywords: Evolutionary, Protein structure, Class 1 integrons, Citrobacter freundii

Identification of the motifs of beta-turns and mutated amino acids studies on BCR (Breakpoint cluster region) protein using Insilico techniques

Recent investigations have rapidly added crucial new insights into the complex functions of the normal BCR gene and of the BCR-ABL chimaera. They are yielding potential therapeutic breakthroughs in the treatment of Philadelphia (Ph) chromosome-positive leukemias. The objective of the present in silico research investigation is to find out whether the functional part (beta-turns) is present in the mutated amino acids of BCR (Breakpoint cluster region) protein. Two significant steps are involved in this study. First, we performed protein sequence modeling of BCR using automated protein modeling servers and the 3D structure was visualized using molecular visualization software and tools. In the second step, the function domains and motifs regions of BCR gene-coded protein is predicted using “PDBsum generate” tool in order to show where exactly the beta-turns lie on the clinically-proven mutated amino acids of BCR protein. The results of our investigation can be used as potential drug binding sites in the field of drug docking studies. It can act as a potential therapeutic agent for Chronic Myeloid Leukemia (CML) type of Leukemia.

Correction: Twists or turns: stabilising alpha vs. beta turns in tetrapeptides

Correction for ‘Twists or turns: stabilising alpha vs. beta turns in tetrapeptides’ by Huy N. Hoang et al., Chem. Sci., 2019, 10, 10595–10600, DOI: 10.1039/C9SC04153B.

MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns

Motivation Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, developed based on advanced deep neural networks, achieved state-of-the-art performance for predicting secondary structures, backbone torsion angles, beta-turns and gamma-turns, respectively. An easy-to-use web service will provide the community a convenient way to use these methods for research and development. Results MUFold-SSW, a new web server, is presented. It provides predictions of protein secondary structures, torsion angles, beta-turns and gamma-turns for a given protein sequence. This server implements MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn, which performed well for both easy targets (proteins with weak sequence similarity in PDB) and hard targets (proteins without detectable similarity in PDB) in various experimental tests, achieving results better than or comparable with those of existing methods. Availability and implementation MUFold-SSW is accessible at http://mufold.org/mufold-ss-angle. Supplementary information Supplementary data are available at Bioinformatics online.

Twists or turns: stabilising alpha vs. beta turns in tetrapeptides

Twisting or turning peptides: ring size and chi angle in side chain cross-linked tetrapeptides together control α- or β-turn structures, which mimic irregular secondary structures in proteins.

A New Clustering and Nomenclature for Beta Turns Derived from High-Resolution Protein Structures

Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I’, II, II’, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta turn conformations from a set of 13,030 turns from 1078 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN andk-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types,BetaTurnLib18, and cross-platform software,BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable fromdunbrack.fccc.edu/betaturnandgithub.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.