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      Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus

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          Abstract

          Background

          The Nonstructural Protein (NSP) 4B of Zika virus of 251 amino acids from (ZIKV/Human/POLG_ZIKVF) with accession number (A0A024B7W1), Induces the production of Endoplasmic Reticulum ER-derived membrane vesicles, which are the sites of viral replication. To understand the physical basis of how proteins fold in nature and to solve the challenge of protein structure prediction, Ab-initio and comparative modeling are crucial tools.

          Results

          The systematic in silico technique, ThreaDom, had only predicted one domain (4 – 190) of NSP4B. I-TASSER, and Alphafold were ranked as the best servers for full-length 3-D protein structure predictions of NSP4B, where the predicted models were evaluated quantitatively using benchmarked metrics including C-score (-3.43), TM-score (0.77949), RMSD (2.73), and Z-score (1.561). The functional and protein binding motifs were realized using motif databases, secondary and surface accessibility predictions combined with Post-Translational Modification Sites (PTMs) prediction. Two highly conserved protein-binding motifs (Flavi NS4B and Bacillus papRprotein), together with three (PTMs) (Casein Kinase II, Myristyl site, and ASN-Glycosylation site) were predicted utilizing the Motif scan and Scanprosite servers. These patterns and PTMs were associated with NSP4B's role in triggering the development of the viral replication complex and its participation in the localization of NS3 and NS5 on the membrane. Only one hit from Structural Classification of Protein (SCOP) matched the protein sequence at positions 10 to 397 and was categorized six-hairpin glycosidases superfamily according to CATH (Class, Architecture, Topology, and Homology). Integrating this NSP4B information with the templates' SCOP and CATH annotations achieves it easier to attribute structure–function/evolution links to both previously known and recently discovered protein structures.

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          Most cited references53

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            SWISS-MODEL: homology modelling of protein structures and complexes

            Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
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              The Phyre2 web portal for protein modeling, prediction and analysis.

              Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
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                Author and article information

                Contributors
                Journal
                J Genet Eng Biotechnol
                J Genet Eng Biotechnol
                Journal of Genetic Engineering & Biotechnology
                Academy of Scientific Research and Technology, Egypt
                1687-157X
                2090-5920
                02 February 2024
                March 2024
                02 February 2024
                : 22
                : 1
                : 100336
                Affiliations
                [a ]Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City 32897, Egypt
                [b ]Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt
                [c ]School of Biotechnology, Badr University in Cairo, Egypt
                Author notes
                Article
                S1687-157X(23)01507-X 100336
                10.1016/j.jgeb.2023.100336
                10860876
                8bb78754-a747-4d89-a85d-4a7e820a97a4
                © 2024 The Author(s). Published by Elsevier B.V. on behalf of Academy of Scientific Research and Technology.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                (ns4b),zika virus,i-tasser,alphafold,scop and cath
                (ns4b), zika virus, i-tasser, alphafold, scop and cath

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