hdANM: a new comprehensive dynamics model for protein hinges

Distinct characteristics of putative archaeal 5-methylcytosine RNA methyltransferases unveil their substrate specificities and evolutionary ancestries

5-Methylcytosine methyltransferases (m5C MTases) are known to be involved in the modification of RNA. Although these enzymes have been relatively well characterized in bacteria and eukarya, a complete understanding of the archaeal counterparts is lacking. In this study, the identification and characterization of archaeal RNA m5C MTases were performed. As a case study, a hyperthermophilic archaeon, Pyrococcus horikoshii OT3, which possesses five putative RNA m5C MTases, was chosen. Among the five putative RNA m5C MTases, two proteins (PH0851 and PH1991) have been characterized as homologs of a bacterial rRNA MTase (RsmB) and eukaryal tRNA MTase (NSUN6), respectively. The in-depth characterization of the remaining three putative RNA m5C MTases (PH1078, PH1374, and PH1537) in this study suggests the presence of the signature architecture and catalytic residues plausibly involved in the binding of their cognate RNA substrates. Additionally, the results also suggest the existence of two RsmB-like proteins (PH0851 and PH1078) belonging to the same subfamily IV of m5C RNA MTase. However, the proteins PH1374 and PH1537 belong to the same subfamily V but bind to different substrates, rRNA and tRNA, respectively. The findings further indicate that archaeal RNA m5C MTases link those from bacteria and eukarya.Communicated by Ramaswamy H. Sarma.

Immunoinformatics Strategy to Develop a Novel Universal Multiple Epitope-Based COVID-19 Vaccine

Currently available COVID vaccines are effective in reducing mortality and severity but do not prevent transmission of the virus or reinfection by the emerging SARS-CoV-2 variants. There is an obvious need for better and longer-lasting effective vaccines for various prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of a broad-spectrum vaccine that can be used to prevent infection by reducing both the transmission rate and re-infection. During the initial phases of SARS-CoV-2 infection, the nucleocapsid (N) protein is one of the most abundantly expressed proteins. Additionally, it has been identified as the most immunogenic protein of SARS-CoV-2. In this study, state-of-the-art bioinformatics techniques have been exploited to design novel multiple epitope vaccines using conserved regions of N proteins from prevalent strains of SARS-CoV-2 for the prediction of B- and T-cell epitopes. These epitopes were sorted based on their immunogenicity, antigenicity score, and toxicity. The most effective multi-epitope construct with possible immunogenic properties was created using epitope combinations. EAAAK, AAY, and GPGPG were used as linkers to connect epitopes. The developed vaccines have shown positive results in terms of overall population coverage and stimulation of the immune response. Potential expression of the chimeric protein construct was detected after it was cloned into the Pet28a/Cas9-cys vector for expression screening in Escherichia coli. The developed vaccine performed well in computer-based immune response simulation and covered a diverse allelic population worldwide. These computational findings are very encouraging for the further testing of our candidate vaccine, which could eventually aid in the control and prevention of SARS-CoV-2 infections globally.

Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier

SUMMARY

A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein Fluctuations in Response to Random External Forces

Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both.

PACKMAN-Molecule: Python toolbox for structural bioinformatics

PACKMAN-molecule is a Structural Bioinformatics toolbox in the form of an Application Programming Interface that contains several utilities that can be used for structural bioinformatics applications. It has already been used in several applications, and its added features and unique object hierarchy make it readily extensible, feature-rich and user-friendly. The tutorial for it is available at: https://py-packman.readthedocs.io/en/latest/tutorials/molecule.html.

Availability and implementation

PACKMAN-Molecule is freely available with an MIT license on GitHub at https://github.com/Pranavkhade/PACKMAN.