Protocol: Predicting and Comparing Protein Structures by Using Alphafold
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Abstract
Since their discovery in the 1990s, single-domain antibodies (sdAbs, nanobodies) have revolutionized the diagnosis and treatment of antibiotic-resistant bacterial diseases. Currently, predicting and modeling nanobody structures using computational tools are essential for screening highly specific antibodies. This research proposal outlines the use of artificial intelligence and visualization tools like PyMOL, Dali, and ColabFold to predict and compare the 3D structures of amino acid chains in the precursor framework of VNAR (Variable domain of new antigen receptor) - a next-generation antigen-binding scaffold. The workflow will focus on two key amino acid chains in the VNAR structure, generating superimposed models to evaluate structural homology among evolutionarily related proteins and assess critical parameters in nanobody design. This in silico VNAR structure prediction pipeline will contribute significantly to the humanization of shark-derived VNARs for therapeutic applications.
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