Prediction of CD44 Structure by Deep Learning-Based Protein Modeling

Camponeschi, Chiara; Righino, Benedetta; Pirolli, Davide; Semeraro, Alessandro; Ria, Francesco; Rosa, Maria Cristina De

doi:10.3390/biom13071047

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…By transforming Cartesian coordinates into spherical coordinates represented by radial distance (ρ), polar angle (θ), and azimuthal angle (ϕ), our method provides a more intuitive representation of protein geometry. This approach facilitates the design of algorithms for protein structure prediction by enabling the extraction of meaningful structural features [10,[121][122][123][124][125][126].…”

Section: Discussionmentioning

confidence: 99%

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

2024

Preprint

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Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

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Section: Discussionmentioning

confidence: 99%

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

2024

Preprint

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“…The 761-amino-acid-long sequences of WT and MT phage tail tubular protein B (TTPB and TTPBm) were respectively modeled using the RoseTTAFold (https://robetta.bakerlab.org/) algorithm for three-dimensional (3D) structure predictions (Baek et al, 2021;Liang et al, 2022;Camponeschi et al, 2023). The PyMOL_1.8.0.3 software was used to generate the 3D structural models of TTPB and TTPBm and perform the structural alignment of the two protein monomers (Rosignoli and Paiardini, 2022).…”

Section: Protein Expression and Molecular Modelingmentioning

confidence: 99%

Host range expansion of Acinetobacter phage vB_Ab4_Hep4 driven by a spontaneous tail tubular mutation

He,

Cao,

et al. 2024

Front. Cell. Infect. Microbiol.

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Bacteriophages (phages) represent promising alternative treatments against multidrug-resistant Acinetobacter baumannii (MDRAB) infections. The application of phages as antibacterial agents is limited by their generally narrow host ranges, so changing or expanding the host ranges of phages is beneficial for phage therapy. Multiple studies have identified that phage tail fiber protein mediates the recognition and binding to the host as receptor binding protein in phage infection. However, the tail tubular-dependent host specificity of phages has not been studied well. In this study, we isolated and characterized a novel lytic phage, vB_Ab4_Hep4, specifically infecting MDRAB strains. Meanwhile, we identified a spontaneous mutant of the phage, vB_Ab4_Hep4-M, which revealed an expanded host range compared to the wild-type phage. A single mutation of G to C was detected in the gene encoding the phage tail tubular protein B and thus resulted in an aspartate to histidine change. We further demonstrated that the host range expansion of the phage mutant is driven by the spontaneous mutation of guanine to cytosine using expressed tail tubular protein B. Moreover, we established that the bacterial capsule is the receptor for phage Abp4 and Abp4-M by identifying mutant genes in phage-resistant strains. In conclusion, our study provided a detailed description of phage vB_Ab4_Hep4 and revealed the tail tubular-dependent host specificity in A. baumannii phages, which may provide new insights into extending the host ranges of phages by gene-modifying tail tubular proteins.

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Computational modeling of protein–carbohydrate interactions: Current trends and future challenges

Pérez

2023

Advances in Carbohydrate Chemistry and Biochemistry

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Prediction of CD44 Structure by Deep Learning-Based Protein Modeling

Cited by 4 publications

References 52 publications

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

Host range expansion of Acinetobacter phage vB_Ab4_Hep4 driven by a spontaneous tail tubular mutation

Computational modeling of protein–carbohydrate interactions: Current trends and future challenges

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