hdANM: a new comprehensive dynamics model for protein hinges

Khade, Pranav M.; Scaramozzino, Domenico; Kumar, Ambuj; Lacidogna, Giuseppe; Carpinteri, Alberto; Jernigan, Robert L.

doi:10.1016/j.bpj.2021.10.017

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…The hinges of the structure play a major role in how the structures deform. The hinges that were present across the entire building did not seem critical and were stable [ 34 ]. The analysis of the B factor revealed no discernible ups and downs, indicating an extremely low loop number.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…Recently, there has been a simpler comprehension of the range of motions available to any given protein structure, constrained simply by the geometry of a particular structure, by using elastic network models ( Atilgan et al , 2001 ; Bahar et al , 1997 ; Tirion, 1996 ). Recent progress is providing simple ways to comprehend protein dynamics based on computing the cohesiveness of different parts of a protein structure, which is based on the local packing densities ( Khade et al , 2019 , Khade, 2021 ). And this has the important advantage of leading to simple ways of visualizing the protein dynamics.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…There are several application examples already published from the use of this submodule. The PACKMAN-Alpha Shape-Based Hinge Prediction ( Khade et al , 2020 ), PACKMAN-Compliance ( Scaramozzino et al , 2020 ) and hdANM ( Khade et al , 2021 ) are three examples.…”

Section: Introductionmentioning

confidence: 99%

PACKMAN-Molecule: Python Toolbox for Structural Bioinformatics

Khade

Jernigan

2022

Bioinformatics Advances

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PACKMAN-molecule is a Structural Bioinformatics toolbox in the form of an Application Programming Interface (API) 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 PACKMAN-Molecule is freely available with an MIT license on GitHub at https://github.com/Pranavkhade/PACKMAN Supplementary information No Supplementary data.

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hdANM: a new comprehensive dynamics model for protein hinges

Cited by 7 publications

References 45 publications

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

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

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

PACKMAN-Molecule: Python Toolbox for Structural Bioinformatics

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